Forest condition in Europe / 2012 Technical Report of ICP

Transcription

Forest Condition in Europe
2012 Technical Report of ICP Forests
2012
International Co-operative Programme on
Assessment and Monitoring of Air Pollution
Effects on Forests (ICP Forests)
Forest Condition
In Europe
Work Report of the:
Thünen Institute for World Forestry
Johann Heinrich von Thünen-Institute
Federal Research Institute for Rural Areas, Forestry and Fisheries
Address: Leuschnerstr. 91, D-21031 Hamburg, Germany
Postal address: P.O. Box: 80 02 09, D-21002 Hamburg, Germany
Phone: +40 / 73962-101
Fax: +40 / 73962-299
E-mail: [email protected]
Internet: http://www.ti.bund.de
Institute for World Forestry
Forest Condition
in Europe
Martin Lorenz, Georg Becher (eds.)
Work report of the Institute for World Forestry 2012/1
Hamburg, December 2012
United Nations Economic Commission for Europe (UNECE)
Convention on Long-Range Transboundary Air Pollution CLRTAP
International Co-operative Programme on Assessment and Monitoring of
Air Pollution Effects on Forests (ICP Forests)
www.icp-forests.net
Citation
Lorenz, M., Becher, G. (eds.). 2012: Forest Condition in Europe, 2012 Technical Report of
ICP Forests. Work Report of the Thünen Institute for World Forestry 2012/1. ICP Forests,
Hamburg, 2012.
Acknowledgements
30 countries supported the preparation of the present report by submission of data and by providing comments and corrections to the text. A complete list of the national and international
institutions participating in or cooperating with ICP Forests is provided in the Annex. Ms U.
Carstens carried out technical and administrative work.
Cover photos: Román Clavijo Garcia (big photo), Anna Kowalska (small photo)
Table of Contents
TABLE OF CONTENTS ........................................................................................................................................ 5
SUMMARY ....................................................................................................................................................... 7
1.
INTRODUCTION ...................................................................................................................................... 9
2.
THE MONITORING SYSTEM ....................................................................................................................10
2.1.
BACKGROUND......................................................................................................................................... 10
2.2 LARGE-SCALE FOREST MONITORING (LEVEL I) ........................................................................................................ 10
2.3 FOREST ECOSYSTEM MONITORING (LEVEL II) ......................................................................................................... 12
2.4.
REFERENCES ........................................................................................................................................... 15
3.
TREE CROWN CONDITION AND DAMAGE CAUSES .................................................................................16
3.1.
LARGE SCALE TREE CROWN CONDITION ........................................................................................................ 16
3.1.1. Methods of the 2011 survey .......................................................................................................... 16
3.1.2. Results of the transnational crown condition survey in 2011 ........................................................ 21
3.1.3. Defoliation trends: time series ....................................................................................................... 32
3.2.
DAMAGE CAUSE ASSESSMENT .................................................................................................................... 50
3.2.1. Background of the survey in 2011 ................................................................................................. 50
3.2.2. Assessment parameters ................................................................................................................. 52
3.2.3. Results in 2011 ............................................................................................................................... 53
3.3.
METHODS OF THE NATIONAL SURVEYS ......................................................................................................... 59
3.4.
REFERENCES ........................................................................................................................................... 59
4.
SULPHATE AND NITROGEN DEPOSITION AND TREND ANALYSES ...........................................................60
4.1.
INTRODUCTION ....................................................................................................................................... 60
4.2.
OBJECTIVES ............................................................................................................................................ 60
4.3.
METHODS .............................................................................................................................................. 61
4.4.
RESULTS ................................................................................................................................................ 62
4.4.1. Current deposition ......................................................................................................................... 62
4.4.2. Temporal trends ............................................................................................................................ 65
4.4.3. Comparison of trend analyses techniques ..................................................................................... 69
4.4.4. Estimation of minimal detectable trend ........................................................................................ 70
4.5.
DISCUSSION ........................................................................................................................................... 71
4.6.
CONCLUSIONS......................................................................................................................................... 73
4.7.
ACKNOWLEDGEMENTS ............................................................................................................................. 73
4.8.
REFERENCES ........................................................................................................................................... 74
5.
EXCEEDANCE OF CRITICAL LIMITS AND THEIR IMPACT ON TREE NUTRITION .........................................77
5.1.
5.2.
6.
OBJECTIVES ............................................................................................................................................ 78
METHODS .............................................................................................................................................. 78
NATIONAL REPORTS ..............................................................................................................................92
6.1.
6.2.
6.3.
6.4.
6.5.
6.6.
6.7.
6.8.
ANDORRA .............................................................................................................................................. 92
BELARUS................................................................................................................................................ 93
BELGIUM ............................................................................................................................................... 93
BULGARIA .............................................................................................................................................. 95
CROATIA ................................................................................................................................................ 95
CYPRUS ................................................................................................................................................. 96
CZECH REPUBLIC ..................................................................................................................................... 97
DENMARK .............................................................................................................................................. 97
6.9.
6.10.
6.11.
6.12.
6.13.
6.14.
6.15.
6.16.
6.17.
6.18.
6.19.
6.20.
6.21.
6.22.
6.23.
6.24.
6.25.
6.26.
6.27.
6.28.
6.29.
6.30.
ESTONIA ................................................................................................................................................ 98
FINLAND ................................................................................................................................................ 99
FRANCE ................................................................................................................................................. 99
GERMANY ............................................................................................................................................ 100
HUNGARY ............................................................................................................................................ 101
ITALY ................................................................................................................................................... 101
LATVIA................................................................................................................................................. 102
LITHUANIA ........................................................................................................................................... 103
REPUBLIC OF MOLDOVA.......................................................................................................................... 104
NORWAY ............................................................................................................................................. 105
POLAND ............................................................................................................................................... 105
ROMANIA............................................................................................................................................. 106
RUSSIAN FEDERATION............................................................................................................................. 106
SERBIA................................................................................................................................................. 107
SLOVAK REPUBLIC .................................................................................................................................. 108
SLOVENIA ............................................................................................................................................. 109
SPAIN .................................................................................................................................................. 109
SWEDEN .............................................................................................................................................. 110
SWITZERLAND ....................................................................................................................................... 111
TURKEY................................................................................................................................................ 112
UKRAINE .............................................................................................................................................. 112
UNITED STATES OF AMERICA ................................................................................................................... 113
ANNEX I: MAPS OF THE TRANSNATIONAL EVALUATIONS ............................................................................. 115
ANNEX I-1: BROADLEAVES AND CONIFERS................................................................................................................. 115
ANNEX I-2: PERCENTAGE OF TREES DAMAGED (2011) ................................................................................................ 116
ANNEX I-3: MEAN PLOT DEFOLIATION OF ALL SPECIES (2011) ...................................................................................... 117
ANNEX I-4: CHANGES IN MEAN PLOT DEFOLIATION (2010-2011)................................................................................. 118
ANNEX II: NATIONAL RESULTS ...................................................................................................................... 119
ANNEX III: ADDRESSES .................................................................................................................................. 139
ANNEX III-1: UNECE AND ICP FORESTS .................................................................................................................. 139
ANNEX III-2: EXPERT PANELS, WG AND OTHER COORDINATING INSTITUTIONS ................................................................ 140
ANNEX III-3: MINISTRIES (MIN) AND NATIONAL FOCAL CENTRES (NFC) ........................................................................ 144
ANNEX III-4: AUTHORS ......................................................................................................................................... 159
Forest Condition in Europe 2012
7
Summary
Of the 42 countries participating in ICP Forests, 29 countries reported for the year 2011 largescale (Level I) monitoring data from about 15,000 plots and forest ecosystem related
(Level II) monitoring data from about 700 plots. The data analysis focused on the impact of
air pollution on forest soils, tree nutrition, and tree crown condition. In this respect emphasis
was laid on the assessment of future risks of air pollution damage to the forests in Europe.
Also considered was the impact of factors other than air pollution such as pests and diseases.
Mean annual throughfall and bulk deposition of S and N was calculated for 289 and 357
Level II plots, respectively. S deposition is highest in central Europe, ranging from the North
Sea coast via central Germany to Poland, the Czech Republic, and the Slovak Republic. High
S deposition along the coast mostly occurs with high Cl deposition indicating that S deposition originates from sea salt. The fact that throughfall is higher than bulk deposition confirms
that the canopy filters sulphur from the air. Similar to S deposition, N deposition is highest in
central Europe but extends further west to France, the United Kingdom, and Ireland, as well
as further south to Switzerland. On about half of the investigated sites there is a statistically
significant decrease in S and N deposition between 2000 and 2010.
For assessing effects of N deposition on the nutrition of trees, the exceedances of harmful
pollutant concentrations (critical limits) in the soil were calculated. Up to 251 Level II plots
were included in the study depending on data availability. Deposition data included throughfall and bulk deposition. Soil solution was sampled using lysimeters in the same intervals as
deposition. For different soil depths annual mean concentrations and their exceedances of
critical limits published in the scientific literature were assessed. Results show a clear relation
between N deposition and the occurrence of high nitrate concentrations below the rooting
zone indicating N saturation at the particular sites. Nutrient imbalances related to high nitrate
concentrations could be substantiated. Mg deficiencies occur more frequently on coniferous
plots with exceedances of critical limits for nitrate in the soil solution. Also for beech trees the
percentage of plots with low Mg amounts is higher on plots with critical limit exceedances.
For spruce, pine, beech and oak there is a tendency towards less optimal Mg/N ratios with
increasing exceedances of critical limits for nitrate in the soil solution. The share of trees with
light green to yellow discolouration is higher when critical limits for nutrient imbalances are
exceeded. The share of trees showing insect damage is related to the exceedance of critical
levels for the BC/Al ratio.
Crown condition is the most widely applied indicator for forest health and vitality in Europe.
Mean defoliation of 135,388 sample trees on 6,807 transnational Level I plots was 19.5%. Of
all trees assessed a share of 20.0% was scored as damaged, i. e. had a defoliation of more than
25%. Of the main species groups, deciduous temperate oak species had by far the highest
mean defoliation (24.4%), followed by deciduous Sub-Mediterranean oak species (22.0%),
and evergreen oak species (21.2%). A mean defoliation of 20.7% was assessed for Fagus sylvatica. Coniferous species had lower defoliation, with Mediterranean lowland pine species
showing 20.4%, followed by Picea abies (18.6%), Pinus sylvestris (18.1%). These figures are
not comparable to those of previous reports because of fluctuations in the plot sample, mainly
due to changes in the participation of countries. Therefore, the long-term development of defoliation was calculated from the monitoring results of those countries which have been submitting data since 1992 every year without interruption. While defoliation of Scots pine and
8
Norway spruce decreased, defoliation of Fagus sylvatica and the oak species groups increased since 1992.
In addition to defoliation, crown condition assessments comprise discolouration as well as
damage caused by biotic and abiotic factors. Among the different damage factors, insects and
fungi are the most frequent ones.
9
1. Introduction
Forest condition in Europe has been monitored since 1986 by the International Co-operative
Programme on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) in the framework of the Convention on Long-range Transboundary Air Pollution
(CLRTAP) under the United Nations Economic Commission for Europe (UNECE). The
number of countries participating in ICP Forests has meanwhile grown to 42 including Canada and the United States of America, rendering ICP Forests one of the largest biomonitoring
networks of the world. ICP Forests has been chaired by Germany from the beginning on. The
Institute for World Forestry of the Johann Heinrich von Thünen-Institute (TI) hosts the Programme Coordinating Centre (PCC) of ICP Forests. The work of ICP Forests was frequently
co-financed by the European Commission (EC) or even conducted in close cooperation with
it.
Aimed mainly at the assessment of effects of air pollution on forests, ICP Forests provides
scientific information to CLRTAP as a basis of legally binding protocols on air pollution
abatement policies. For this purpose ICP Forests developed a harmonised monitoring approach comprising a large-scale forest monitoring (Level I) as well as a forest ecosystem forest monitoring (Level II) approach laid down in the ICP Forests Manual. The participating
countries submit their monitoring data to PCC for validation, storage, and analysis.
While ICP Forests - in line with its obligations under CLRTAP - focuses on air pollution effects, its monitoring approach was extended towards assessments of forest information related
to carbon budgets, climate change, and biodiversity. This was accomplished in close cooperation with the EC LIFE project “FutMon” in the years 2009 to 2011. ICP Forests delivers also
information to processes of international environmental politics other than CLRTAP. This
holds true in particular for the provision of information on several indicators for sustainable
forest management laid down by Forest Europe (FE).
The present report describes the results of the latest analyses of the ICP Forests large-scale
(Level I) and forest ecosystem related (Level II) monitoring data. It is structured as follows:
Chapter 2 describes the Level I and Level II monitoring systems. Chapter 3 presents results of
the 2011 crown condition survey including assessments of different damage causes. In Chapter 4 the spatial and temporal variation of sulphur and nitrogen deposition are described.
Chapters 5 focus on the risks posed to forests by air pollution and climate change. Chapter 6
consists of national reports by the participating countries, focussing on crown condition in
2011 as well as its development and its causes. Maps, graphs and tables concerning the transnational and the national results are presented in the Annexes.
10
2. The monitoring system
2.1. Background
Martin Lorenz, Oliver Granke1
Forest monitoring in Europe has been conducted for 27 years according to harmonised methods and standards by the International Cooperative Programme on Assessment and Monitoring of Air Pollution effects on Forests (ICP Forests) of the Convention on Long-range Transboundary Air Pollution (CLRTAP) under the United Nations Economic Commission for
Europe (UNECE). The monitoring results meet the scientific information needs of CLRTAP
for clean air policies under UNECE. According to its strategy for the years 2007 to 2015, ICP
Forests pursues the following two main objectives:
1. To provide a periodic overview of the spatial and temporal variation of forest condition in relation to anthropogenic and natural stress factors (in particular air pollution) by means of European-wide (transnational) and national large-scale representative monitoring on a systematic network (monitoring intensity Level I).
2. To gain a better understanding of cause-effect relationships between the condition of
forest ecosystems and anthropogenic as well as natural stress factors (in particular
air pollution) by means of intensive monitoring on a number of permanent observation selected in most important forest ecosystems in Europe (monitoring intensity
Level II).
The complete methods of forest monitoring by ICP Forests are described in detail in the
“Manual on methods and criteria for harmonised sampling, assessment, monitoring and
analysis of the effects of air pollution on forests” (ICP Forests 2010). For many years forest
monitoring according to the ICP Forests Manual was conducted jointly by ICP Forests and the
European Commission (EC) based on EU - cofinancing under relevant Council and Commission Regulations. The monitoring results are also delivered to processes and bodies of international forest and environmental policies other than CLRTAP, such as Forest Europe (FE), the
Convention on Biological Diversity (CBD), the UN-FAO Forest Resources Assessment
(FRA), and EUROSTAT of EC. In order to better meet the new information needs with respect to carbon budgets, climate change, and biodiversity, the forest monitoring system was
further developed in the years 2009 to 2011 within the project “Further Development and Implementation of an EU-level Forest Monitoring System” (FutMon) under EU-cofinancing.
The following chapters describe briefly the selection of sample plots and the surveys on the
revised Level I and Level II monitoring networks.
2.2 Large-scale forest monitoring (Level I)
The large-scale forest monitoring grid consists of more than 7500 plots. The selection of
Level I plots is within the responsibility of the participating countries, but the density of the
plots should resemble that of the previous 16 x 16 km grid. For this reason, the number of
plots in each country should be equal to the forest area of the country (in km2) divided by 256.
1
See addresses in Annex III-4
11
By the end of FutMon in June 2011, 58% of the Level I plots in the EU-Member States were
coincident with NFI plots. No coincidence with NFI plots was given for 29% of the plots. It is
expected, however, that a number of countries will merge these plots with NFI plots at a later
date. For the remaining plots no information was made available (Fig. 2.2-1).
Figure 2.2-1: Spatial distribution of the large-scale plots under FutMon. Green colour implies a coincidence with
NFI plots.
On most of the Level I plots tree crown condition is assessed every year. In 1995, element
contents in needles and leaves were assessed on about 1500 plots and a forest soil condition
survey was carried out on about 3500 plots. The Level I soil condition survey was repeated on
about 5300 plots in 2005 and 2006 and the species diversity of forest ground vegetation was
assessed on about 3400 plots in 2006 under the Forest Focus Regulation of EC within the
BioSoil project (Fig. 2.2-2).
12
Figure 2.2-2: Spatial distribution of the large-scale plots under FutMon. Green colour implies inclusion in the
BioSoil project under the Forest Focus Regulation of EC.
2.3 Forest ecosystem monitoring (Level II)
The number of forest ecosystem monitoring (Level II) plots in the data base is 938 including
plots with different assessment intensities and a number of abandoned plots as well. On the
plots up to 17 surveys are conducted (Tab. 2.3.-1). Of these surveys many are not conducted
continuously or annually, but are due only every few years. The complete set of surveys,
however, is carried out on only about 100 Level II “core plots”. The map in Fig. 2.3-1 shows
those plots on which crown condition was assessed in 2009, coming close to the total of all
Level II plots assessed in 2009. Moreover, the map indicates the locations of Level II plots of
previous years.
13
Table 2.3-1: Surveys and assessment frequencies in 2010
Survey
Crown condition
Data submitted for
2010
565
Assessment frequency
Annually
Foliar chemistry
115
Every two years
Soil condition
60
Every ten years
Soil solution chemistry
206
Continuously
Tree growth
100
Every five years
Deposition
311
Continuously
Ambient air quality (active)
164
Continuously
Ambient air quality (passive)
211
Continuously
Ozone induced injury
124
Annually
Meteorology
249
Continuously
Phenology
131
Several times per
year
Ground vegetation
254
Every five years
Litterfall
172
Continuously
Nutrient budget of ground
vegetation
92
Once
Leaf Area Index
145
Once
Soil Water
44
Once
Extended Tree Vitality
117
Annually/ Continuously
14
Figure 2.3-1: Level II plots with crown condition assessments in 2009. Also shown are plots with other surveys
and of previous years.
15
2.4. References
ICP Forests (2010) Manual on methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests,
Hamburg. ISBN: 978-3-926301-03-1, [http://www.icp-forests.org/Manual.htm]
16
3. Tree crown condition and damage causes
Georg Becher, Martin Lorenz, Stefan Meining, Richard Fischer1
3.1. Large scale tree crown condition
3.1.1. Methods of the 2011 survey
The annual transnational tree condition survey was conducted on 6 807 plots in 28 countries
including 19 EU-Member States (Tab. 3.1.1-1). The assessment was carried out under national responsibilities according to harmonized methods laid down in ICP Forests. Prior to the
evaluation all data were checked for consistency by the participating countries and submitted
online to the Programme Coordinating Centre at the Institute for World Forestry in Hamburg,
Germany.
Table 3.1.1-1: Number of sample plots assessed for crown condition from 1999 to 2011
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Rep.
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal*
Romania
Slovak Rep.
Slovenia
Spain**
Sweden
United Kingdom
EU
Andorra
Belarus
Croatia
Moldova
Montenegro
Norway
Russian Fed.
Serbia
Switzerland
Turkey
Total Europe
1999
130
30
114
2000
130
29
108
139
23
91
457
544
433
93
62
20
239
98
67
4
11
431
149
238
110
41
611
764
85
4.984
139
21
90
453
516
444
93
63
20
255
94
67
4
11
431
149
235
111
41
620
769
89
4.982
11
431
150
232
110
41
620
770
86
5.004
408
84
10
408
83
10
381
1
62
19
247
95
64
4
11
433
142
231
108
41
620
776
86
4.887
408
81
10
407
80
406
78
382
408
414
411
442
460
49
49
49
49
103
48
130
48
5.916
5.914
5.960
5.947
5.933
6.152
See addresses in Annex III-4
2003
131
29
105
15
140
20
93
453
515
447
Number of plots assessed
2004
2005
2006
136
136
135
29
29
27
103
102
97
15
15
15
140
138
136
20
22
22
92
92
92
594
605
606
511
509
498
451
451
423
87
73
73
73
19
18
21
255
238
251
95
92
93
63
62
62
4
4
4
11
11
11
433
432
376
139
125
124
226
229
228
108
108
107
42
44
45
620
620
620
775
784
790
85
84
82
5.039 5.110
4.938
3
3
406
403
398
84
85
88
2002
133
29
98
15
140
20
92
457
518
447
91
62
20
258
97
66
4
11
433
151
231
110
39
620
769
86
4.997
* including Azores, **including Canares
2001
130
29
108
15
139
21
89
454
519
446
92
63
20
265
97
66
2007
2008
2009
27
104
15
132
19
93
593
504
420
26
98
15
136
19
92
475
508
423
72
30
238
93
62
4
72
31
236
92
70
4
26
159
15
133
16
92
886
500
412
97
73
32
252
207
72
2010
135
9
159
15
132
17
97
932
532
411
98
71
29
253
207
75
458
453
11
376
11
374
227
108
44
620
857
4.727
3
416
92
49
496
295
119
47
563
6.807
218
107
44
620
32
3.885
3
400
83
3.478
3
400
84
5.215
3
409
83
239
108
44
620
830
76
5.474
3
410
83
463
476
481
129
48
127
48
6.235
6.065
125
48
43
5.063
123
48
396
5.013
487
365
122
48
560
7.292
49
491
288
121
48
554
7.521
108
620
2011
9
159
15
136
18
98
717
544
404
72
253
203
77
367
242
109
44
620
640
17
Similar to the previous Forest Condition Report, data on forest damage causes collected in
2011 are analysed and detailed results presented in Chapter 3.2.
The spatial distribution of the plots assessed in 2011 is shown in Fig. 3.1.1-1.For certain
analyses of defoliation, the Level I plots are stratified according to the European Forest Types
(EFT). The system of EFT was developed in 2006 by the European Environment Agency
(EEA) of the European Union in cooperation with experts from some European countries coordinated by the Italian Academy of Forest Sciences. After improvements and refinements
based on experts’ knowledge and information gained from NFIs plots, forest maps and forest
management plans, the classification of European forests into forest types became operational.
The system of the European Forest Types consists of 14 categories, representing groups of
ecologically distinct forest communities dominated by specific assemblages of trees. The
classification is conceived to categorize stocked forest land, with the help of classification
keys mainly based on forest dominant tree species (Tab. 3.1.1-2).
Figure 3.1.1-1: Plots according to European Forests Types (2011).
Table 3.1.1-2: Description of the European Forest Types (EFT).
Forest type category
1. Boreal forest
2. Hemiboreal and
nemoral coniferous
and mixed broadleaved-coniferous
forest
3. Alpine forest
4. Acidophilous oak
and oakbirch forest
5. Mesophytic deciduous forest
6. Beech forest
7. Mountainous beech
forest
8. Thermophilous deciduous forest
9. Broadleaved evergreen forest
10. Coniferous forests
of the Mediterranean,
Anatolian and
Macaronesian regions
11. Mire and swamp
forest
12. Floodplain forest
13. Non-riverine alder,
birch or aspen forest
14. Introduced tree
species Forest
Main characteristics
Extensive boreal, species-poor forests, dominated by Picea abies and Pinus
sylvestris. Deciduous trees including birches (Betula spp.), aspen (Populus
tremula), rowan (Sorbus aucuparia) and willows (Salix spp.) tend to occur as
early colonisers.
Latitudinal mixed forests located in between the boreal and nemoral (or temperate) forest zones with similar characteristics to EFT 1, but a slightly higher
tree species diversity, including also temperate deciduous trees like Tilia cordata, Fraxinus excelsior, Ulmus glabra and Quercus robur. Includes also: pure
and mixed forests in the nemoral forest zone dominated by coniferous species
native within the borders of individual FOREST EUROPE member states like
Pinus sylvestris, pines of the Pinus nigra group, Pinus pinaster, Picea abies,
Abies alba.
High-altitude forest belts of central and southern European mountain ranges,
covered by Picea abies, Abies alba, Pinus sylvestris, Pinus nigra, Larix decidua,
Pinus cembra and Pinus mugo. Includes also the mountain forest dominated by
birch of the boreal region.
Scattered occurrence associated with less fertile soils of the nemoral forest
zone; the tree species composition is poor and dominated by acidophilous oaks
(Q. robur, Q. petraea) and birch (Betula pendula).
Related to medium rich soils of the nemoral forest zone; forest composition is
mixed and made up of a relativelylarge number of broadleaved deciduous trees:
Carpinus betulus, Quercus petraea, Quercus robur, Fraxinus, Acerand Tilia
cordata.
Widely distributed lowland to submountainous beech forest. Beech, Fagus
sylvatica and F. orientalis (Balkan) dominate, locally important is Betula pendula.
Mixed broadleaved deciduous and coniferous vegetation belt in the main European mountain ranges. Speciescomposition differs from EFT 6, including Picea
abies, Abies alba, Betula pendula and mesophytic deciduous tree species. Includes also mountain fir dominated stands.
Deciduous and semi-deciduous forests mainly of the Mediterranean region
dominated by thermophilous species, mainly of Quercus; Acer, Ostrya, Fraxinus, Carpinus species are frequent as associated secondary trees. Includes also
Castanea sativa dominated forest.
Broadleaved evergreen forests of the Mediterranean and Macaronesian regions
dominated by sclerophyllous or lauriphyllous trees, mainly Quercus species.
Varied group of coniferous forests in Mediterranean, Anatolian and Macaronesian regions, from the coast to high mountains. Dry and often poorly-developed
soils limit tree growth. Several tree species, including a number of endemics, of
Pinus, Abies and Juniperus species.
Wetland forests on peaty soils widely distributed in the boreal region. Water
and nutrient regimes determine the dominant tree species: Pinus sylvestris,
Picea abies or Alnus glutinosa.
Riparian and riverine species-rich forests characterised by different assemblages of species of Alnus, Betula, Populus, Salix, Fraxinus, Ulmus.
Pioneer forests dominated by Alnus, Betula or Populus.
Forests dominated by introduced trees above categories. Introduced tree species
can be identified at regional (recommended) or national level.
19
20
Defoliation: scientific background for its assessment and analysis
Defoliation reflects a variety of natural and human induced environmental influences. It
would therefore be inappropriate to attribute it to a single factor such as air pollution without
additional evidence. As the true influence of site conditions and the share of tolerable defoliation cannot be quantified precisely, damaged trees cannot be distinguished from healthy ones
only by means of a certain defoliation threshold. Consequently, the 25% threshold for defoliation does not necessarily identify trees damaged in a physiological sense. Some differences in
the level of damage across national borders may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of trends over time.
Natural factors strongly influence crown condition. As also stated by many participating
countries, air pollution is thought to interact with natural stressors as a predisposing or accompanying factor, particularly in areas where deposition may exceed critical loads for acidification (CHAPPELKA and FREER-SMITH, 1995, CRONAN and GRIGAL, 1995, FREERSMITH, 1998).
It has been suggested that the severity of forest damage has been underestimated as a result of
the replacement of dead trees by living trees in the course of regular forest management activities. However, detailed statistical analyses of the results of 10 monitoring years have revealed that the number of dead trees has remained so small that their replacement has not influenced the results notably (LORENZ et al., 1994).
Classification of defoliation data
The results of the evaluations of the crown condition data are presented in terms of mean plot
defoliation or the percentages of the trees falling into 5%-defoliation steps. In previous presentations of survey results, partly the traditional classification of both defoliation and discolouration had been applied, although it is considered arbitrary by some countries. This classification (Tab. 3.1.1-3) is a practical convention, as real physiological thresholds cannot be defined.
Table 3.1.1-3: Defoliation and discolouration classes according to
UNECE and EU classification
Defoliation class
0
1
2
3
4
Discolouration
class
0
1
2
3
4
needle/leaf loss
up to 10 %
> 10 - 25 %
> 25 - 60 %
> 60 - < 100 %
100 %
foliage
discoloured
up to 10 %
> 10 - 25 %
> 25 - 60 %
> 60 %
degree of defoliation
none
slight (warning stage)
moderate
severe
dead
degree of discolouration
none
slight
moderate
severe
dead
In order to discount background perturbations which
might be considered minor, a
defoliation of >10-25% is considered a warning stage, and a
defoliation > 25% is taken as a
threshold for damage. Therefore, in the present report a
distinction has sometimes only
been made between defoliation
classes 0 and 1 (0-25% defoliation) on the one hand, and
classes 2, 3 and 4 (defoliation >
25%) on the other hand.
Classically, trees in classes 2, 3
and 4 are referred to as "damaged", as they represent trees with considerable defoliation. In
the same way, the sample points are referred to as "damaged" if the mean defoliation of their
trees (expressed as percentages) falls into class 2 or higher. Otherwise the sample point is
considered as "undamaged". The most important results have been tabulated separately for all
countries participated (called "all plots") and for the participating EU-Member States.
21
3.1.2. Results of the transnational crown condition survey in 2011
On each sampling point sample trees were selected according to national procedures and assessed for defoliation. According to Tab. 3.1.2-1 the defoliation assessment was carried out in
28 countries including 135 388 trees. The figures in Tab. 3.1.2-1 are not necessarily identical
with those published in the reports of the past years since in case of a restructure of the national observation networks a resubmission of older data is possible.
Table 3.1.2-1: Number of sample trees from 1999 to 2011 according to the current data base
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Rep.
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Netherlands
Poland
Portugal
Romania
Slovak Rep.
Slovenia
Spain
Sweden
United Kingdom
1999
3.535
696
4.344
2000
3.506
686
4.197
3.475
552
2.184
8.662
10.883
13.466
2.192
1.470
417
6.710
2.348
1.613
96
225
8.620
4.470
5.712
5.063
984
14.664
11.135
3.475
504
2.160
8.576
10.317
13.722
2.192
1.488
420
7.128
2.256
1.609
96
218
8.620
4.470
5.640
5.157
984
14.880
11.361
2.039
2.136
2001
3.451
682
4.174
360
3.475
504
2.136
8.579
10.373
13.478
2.168
1.469
420
7.350
2.325
1.597
2003
3.470
684
3.836
360
3.500
480
2.228
8.482
10.298
13.572
231
8.620
4.500
5.568
5.054
984
14.880
11.283
2002
3.503
684
3.720
360
3.500
480
2.169
8.593
10.355
13.534
2.144
1.446
424
7.165
2.340
1.583
96
232
8.660
4.530
5.544
5.076
936
14.880
11.278
2.064
2.064
2.064
1.446
403
6.866
2.293
1.560
96
231
8.660
4.260
5.544
5.116
983
14.880
11.321
Number of sample trees
2004
2005
2006
3.586
3.528
3.425
681
676
618
3.629
3.592
3.510
360
361
360
3.500
3.450
3.425
480
528
527
2.201
2.167
2.191
11.210 11.498 11.489
10.219 10.129
9.950
13.741 13.630 10.327
2.054
1.710
1.662
1.674
400
382
445
7.109
6.548
6.936
2.290
2.263
2.242
1.487
1.512
1.505
96
97
96
232
232
230
8.660
8.640
7.520
4.170
3.749
3.719
5.424
5.496
5.472
5.058
5.033
4.808
1.006
1.056
1.069
14.880 14.880 14.880
11.255 11.422 11.186
2.040
2.016
1.968
2007
2008
2009
616
3.569
360
3.300
442
2.209
11.199
10.074
10.241
599
3.304
360
3.400
452
2.196
8.812
10.138
10.347
1.650
646
6.636
2.228
1.507
96
1.661
679
6.579
2.184
1.688
96
599
5.560
362
3.325
384
2.202
7.182
9.949
10.088
2.289
1.668
717
6.794
3.911
1.734
2010
3.087
216
5.569
360
3.300
408
2.348
7.946
10.584
10.063
2.311
1.626
641
8.338
3.888
1.814
9.160
9.036
247
7.520
227
7.482
7.342
5.448
4.944
1.056
14.880
2.591
5.736
4.831
1.052
14.880
2.742
5.808
5.218
1.057
14.880
2.057
5.232
4.904
1.056
14.880
768
4.956
14.880
2011
230
5.583
360
3.400
432
2.372
4.217
11.111
9.635
1.702
8.454
3.778
1.846
1.803
EU 115.555 115.798 115.725 115.296 112.633 115.424 116.601 109.572 90.773 81.367 93.450 101.252 89.482
Andorra
72
74
72
72
73
72
72
Belarus
9.745
9.763
9.761
9.723
9.716
9.682
9.484
9.373
9.424
9.438
9.615
9.617
9.583
Croatia
2.015
1.991
1.941
1.910
1.869
2.009
2.046
2.109
2.013
2.015
1.991
1.992
2.208
Moldova
259
234
234
Montenegro
1.176
1.176
Norway
4.052
4.051
4.304
4.444
4.547
5.014
5.319
5.525
5.824
6.085
6.014
6.330
6.463
Russian Fed.
11.016
8.958
9.275
Serbia
2.274
2.915
2.995
2.902
2.860
2.788
2.751
2.786
2.742
Switzerland
857
855
834
827
806
748
807
812
790
773
801
795
1.105
Turkey
941
9.291 13.156 12.974 13.282
Total Europe 132.483 132.692 132.799 132.200 131.845 135.864 137.252 130.367 112.697 111.829 138.867 145.952 135.388
* including Azores, ** including Canares
The main results summarized in Tab. 3.1.2-2 show that the mean defoliation of all trees assessed in Europe was 19.5%. Broadleaved trees showed a higher mean defoliation (20.4%)
than conifers (18.7%). With a share of 24.4% the deciduous temperate oak is the most damaged species group followed by the Mediterranean oak (24.2%) and Fagus sylvatica (20.7%).
The spatial distribution of the two species groups depicted in Annex I-1 shows that in 2011
61.3% of the plots were dominated by coniferous and 38.7% by broadleaved trees.
The maps in Annex I-2 and Annex I-3 indicate that defoliation is highest on plots located in
central and southern Europe. The largest shares (58.0%) are plots with mean defoliation ranging from 11 to 25%. The percentage of trees damaged i.e. trees defoliated by 25% and more
22
is relatively low in northern Europe, whereas clusters of severely damaged trees are found in
some parts of Czech Republic and France (Annex I-2).
Table 3.1.2-2: Percentages of trees in defoliation classes and mean defoliation for broadleaves, conifers and all
species
EU
Total
Europe
Species type
broadleaves
conifers
all species
Fagus sylvatica
Deciduous temperate oak
Deciduous (sub-)
mediterranean oak
Evergreen oak
broadleaves
Pinus sylvestris
Picea abies
Mediterranean lowland
pines
conifers
all species
27.6
30.8
29.2
34.6
Percentage of trees in defoliation class
>10-25
0-25
>25-60
>60
dead
46.6
74.1
22.6
2.3
0.9
46.7
77.5
20.3
1.4
0.8
46.6
75.8
21.4
1.9
0.9
39.8
74.4
23.4
1.8
0.5
>25
25.9
22.5
24.2
25.6
Defoliation
mean median No of trees
22.2
20
43 515
20.4
15
44 855
21.3
20
88 370
20.7
20
11 764
20.7
47.2
68.0
28.9
1.8
1.4
32.0
24.4
20
9 025
26.7
20.7
34.2
35.1
43.3
49.2
61.5
43.7
49.1
34.0
75.8
82.3
77.9
84.3
77.3
20.8
14.9
19.1
14.0
19.7
2.5
2.1
2.1
0.9
1.7
0.9
0.7
1.0
0.9
1.3
24.2
17.7
22.1
15.7
22.7
22.0
21.2
20.4
18.1
18.6
20
15
15
15
15
8 256
4 744
62 221
31 010
18 901
24.6
36.3
35.3
59.2
45.5
44.7
83.8
81.8
80.0
13.7
15.9
17.4
0.9
1.2
1.6
1.6
1.0
1.0
16.2
18.2
20.0
20.4
18.7
19.5
15
15
15
8 847
71 440
133 661
For the 5% defoliation classes including dead trees a frequency distribution was calculated.
Fig. 3.1.2-1 indicates that about 20% of all species were defoliated by 15%. More conifers
than broadleaves fell in 2011 in defoliation classes of up to 20%, whereas deciduous trees are
more frequently represented in defoliation classes above 20%.
25
20
Percentage of trees
broadleaves
conifers
all species
15
10
5
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 100
Tree defoliation [%]
Figure 3.1.2-1: Relative frequency distribution of all trees assessed in 2011 in 5% defoliation steps
23
For the first time crown condition data were evaluated according to the European Forest
Types (EFT) described in Tab. 3.1.1-2 of this report. Similar to other evaluations mean defoliation and the share of trees in damage categories were calculated in each of the 14 forest
types. As indicated in Tab. 3.1.2-3 the highest mean defoliation was found in the broadleaved
evergreen forests (24.2%), corresponding with the highest share of trees defoliated by 25%
and more. The values of mean defoliation of the most forest types vary between 19 and 21%.
The healthiest trees in terms of mean defoliation are in boreal forests with mean defoliation of
14.4% and the percentage of healthy trees of roughly 57%.
Table 3.1.2-3: Percentages of trees in defoliation classes and mean defoliation according to European Forest
Types (EFT)
Forest type
Code
Name
Percentage of trees in defoliation class
0-10
>10-25
0-25
>25-60
>60
dead
mean
defoliation
>25
1 Boreal forest
56.8
32.4
89.2
7.8
1.3
1.7
10.8
14.4
Hemiboreal forest and nemoral coniferous and mixed
2 broadleaved-coniferous forest
31.6
49.8
81.4
17.4
0.9
0.3
18.6
18.6
3 Alpine coniferous forest
29.5
40.7
70.2
26.8
2.2
0.9
29.8
22.6
4 Acidophilous oak and oak‑birch forest
17.9
65.8
83.8
13.6
0.8
1.9
16.2
21.6
5 Mesophytic deciduous forest
31.8
41.2
73.0
23.7
2.2
1.1
27.0
21.6
6 Beech forest
37.5
40.1
77.6
20.3
1.5
0.6
22.4
19.4
7 Mountainous beech forest
30.6
42.5
73.1
24.1
2.4
0.4
26.9
21.8
8 Thermophilous deciduous forest
28.3
49.3
77.6
19.0
2.2
1.2
22.4
21.7
9 Broadleaved evergreen forest
24.2
43.2
67.4
28.8
3.4
0.5
32.6
24.2
Coniferous forests of the Mediterranean, Anatolian and
10 Macaronesian regions
30.5
57.2
87.7
10.4
0.8
1.2
12.3
18.5
11 Mire and swamp forest
45.7
40.1
85.8
12.4
1.0
0.9
14.2
16.4
12 Floodplain forest
31.3
44.2
75.5
20.9
2.2
1.5
24.5
21.4
13 Non riverine alder, birch, or aspen forest
36.0
55.2
91.3
7.3
0.9
0.5
8.7
16.3
14 Plantations and self-sown exotic forest
42.2
39.7
81.9
14.0
1.2
2.8
18.1
19.4
In view of the species richness (about 130) recorded within the transnational forest monitoring
only the most abundant species could be evaluated. For other, also important but less abundant species the following groups were created and evaluated in this report:
Deciduous temperate oak: (Quercus robur and Q. petraea) accounting together for
6.7% of the assessed trees,
 Mediterranean lowland pines: (Pinus brutia, P. pinaster, P. halepensis and P. pinea)
accounting together for 6.1% of the assessed trees,
 Deciduous (sub-) temperate oak: (Quercus frainetto, Q. pubescens, Q. pyrenaica
and Q. cerris) accounting together for 5.5% of the assessed trees,
 Evergreen oak: (Quercus coccifera, Q. ilex, Q. rotundifolia and Q. suber) accounting
together for 3.9% of the assessed trees.
For all evaluations of related to a particular tree species a criterion had to be set up to decide if
a given plot represents this species or not. This criterion was that the number of trees of the
particular species had to be three or more per plot. The mean plot defoliation for the particular

24
species was then calculated as the mean defoliation of the trees of the species on that plot
based on sample size N ≥ 3.
In Fig. 3.1.2-2 to 3.1.2-8 mean plot defoliation for Pinus sylvestris, Picea abies, Fagus sylvatica and the four species groups Deciduous temperate oak, Mediterranean lowland pines,
Deciduous (sub-) temperate oak and Evergreen oak (see above) is mapped. The spatial
distribution of these species and species groups will be described in relation to Tab. 3.1.2-2.
According to this table the highest level of mean defoliation had deciduous temperate oaks
(24.4%). For the evergreen oaks a mean defoliation of 21.2% (Tab. 3.1.2-2) was calculated
but the majority of the plots namely 71.1% have a mean defoliation lying between 11 and
25% (Fig. 3.1.2-8).
The mean defoliation value of Pinus sylvestris is relatively low (18.1%). The spatial distribution of plots defoliated by 26 to 40% shows clusters in central Europe, whereas healthy plots
(0-10% defoliation) can mostly be found in Scandinavia, (Fig. 3.1.2-2).
Similar to Pinus sylvestris heavily damaged Picea abies plots are concentrated in central
Europe. The distribution of healthy plots resembles that of Pinus sylvestris (Fig. 3.1.2-3).
Clustered occurrence of severely damaged Fagus sylvatica plots is in Germany (Fig. 3.1.2-5)
In the group of deciduous (sub-) Mediterranean oaks the most affected plots are in southern
Europe. The share of plots in this species group showing negligible signs of crown transparency is less than 14 % (Fig. 3.1.2-7).
Figure 3.1.2-2: Mean plot defoliation for Pinus sylvestris (2011)
25
26
Figure 3.1.2-3: Mean plot defoliation for Picea abies (2011)
27
Figure 3.1.2-4: Mean plot defoliation for Mediterranean lowland pine (Pinus brutia, Pinus halepensis, Pinus
pinaster, Pinus pinea), 2011
28
Figure: 3.1.2-5: Mean plot defoliation for Fagus sylvatica (2011)
29
Figure 3.1.2-6: Mean plot defoliation for deciduous temperate oak (Quercus petraea and Quercus robur), 2011
30
Figure 3.1.2-7: Mean plot defoliation for deciduous (sub-) Mediterranean oak (Quercus cerris, Quercus
frainetto, Quercus pubescens, Quercus pyrenaica), 2011
31
Figure 3.1.2-8: Mean plot defoliation for evergreen oak, temperate oak (Quercus coccifera, Quercus ilex,
Quercus rotundifolia, quercus suber, 2011
32
3.1.3. Defoliation trends: time series
The development of defoliation is calculated assuming that the sample trees of each survey
year reflect the influence of forest conditions. Studies carried out in the past years show that
the fluctuation of trees in this sample (due to the exclusion of dead and felled trees as well as
inclusion of replacement trees) does not cause bias or other distortions of the results over the
years. However, fluctuations due to the inclusion of newly participating countries must be
excluded, because forest condition among countries can deviate greatly. For this reason, the
development of defoliation can only be calculated for defined sets of countries. Different
lengths of time series require different sets of countries, because at the beginning of the surveys the number of participating countries was much smaller than it is today.
For the present evaluation the following three time periods and the following countries were
selected for tracing the development of defoliation:
Period 1991-2011 (“long term period”): Belgium, Czech Republic, Denmark,
Finland, France1, Germany, Hungary, Ireland, Italy, Latvia, Poland, Slovak Republic,
Spain, and Switzerland.
 Period 1997-2011 (“many countries”): Belarus, Belgium, Bulgaria, Croatia, Czech
Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Norway, Poland, Slovak Republic, Spain, and Switzerland.
 Period 2002-2011 (“short term period used to calculate the trend of the mean plot
defoliation”): Belarus, Belgium, Bulgaria, Croatia, Czech Republic, Cyprus, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Latvia, Lithuania,
Norway, Poland, Slovak Republic, Spain, and Switzerland.
Several countries could not be included in one of the three time periods because of changes in
their tree sample sizes, their assessment methods or missing assessments in certain years. Development of defoliation is presented for the periods 1992-2011 and 1997-2011 in graphs and
in tables. Graphs show the fluctuations of mean defoliation and shares of trees in defoliation
classes over time.

The maps depict trends in mean defoliation from 2002-2011. Whereas all plots of the countries mentioned above are included for the two respective time periods in graphs, the maps of
the trend analysis only represent plots within these countries that were included in all of the
surveys. In the last years plots were shifted within Finland and parts of northern Germany
(Brandenburg). These plots are not depicted in the maps but the countries are included in the
time series calculation.
The temporal development of defoliation is expressed on maps as the slope of linear regression of mean defoliation against the observation year. It can be interpreted as the mean annual
change in defoliation. These slopes were statistically tested and considered as ‘significant’
only if there was at least 95% probability that they are different from zero.
Besides the temporal development, also the change in the results from 2010 to 2011 was calculated. In this case, changes in mean defoliation per plot are called ‘significant’ only if the
significance at the 95% probability level was proven in a statistical test.
1
Methodological changes in the first years of the assessments
33
The spatial pattern of the changes in mean defoliation from 2010 to 2011 across Europe is
shown in Annex I-4. On 77.9% of the plots between 2010 and 2011 no statistically significant
differences in mean plot defoliation were detected. The share of plots with increasing defoliation was 10.7%, the share of plots with a decrease 11.4%.
3.1.3.1.
All species
For all species, the two time series show very similar trends for mean defoliation due to the
fact that the countries included in the short time series were also included in the evaluation of
the long time series (Fig. 3.1.3.1-1 and Fig. 3.1.3.1-2). For evergreen oak and Mediterranean
lowland pines there was hardly any difference in sample sizes on which evaluations of the
different time series were based. The largest differences occurred for Pinus sylvestris and
Picea abies the sample sizes for the long time series being 70% smaller than that of the
shorter time series.
30
mean defoliation [%]
25
20
15
Common beech
10
Deciduous (sub-) mediterranean oak
Evergreen oak
Scots pine
5
Norway spruce
2008
2009
2010
2011
2008
2009
2010
2011
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Mediterranean lowland pines
0
Figure 3.1.3.1-1: Mean defoliation of main species 1992-2011
30
20
15
Figure 3.1.3.1-2: Mean defoliation of main species 1997-2011
2007
2006
2005
2004
2000
1999
1998
1997
1996
1995
1994
1993
0
1992
5
2003
10
2002
Common beech
Deciduous (sub-) mediterranean oak
Evergreen oak
Scots pine
Norway spruce
Mediterranean lowland pines
2001
25
34
Since 1992 mean defoliation of the evaluated tree species developed very differently. With
the exception of Picea abies and Pinus sylvestris, all tree species showed a sharp increase in
mean defoliation in the first years of the study. Mean defoliation of Picea abies, Fagus
sylvatica and the deciduous temperate oaks reached largest values after the extremely dry and
warm summer in 2003. In all samples studied, deciduous temperate oaks and deciduous (sub-)
Mediterranean oaks exhibited the highest mean defoliation over the last decade. In contrast,
Pinus sylvestris clearly showed the lowest mean defoliation from all evaluated species.
Trends in mean plot defoliation for all tree species for the period 2002-2011 are mapped in
Fig. 3.1.3.1-3. The percentage of plots with clearly increasing defoliation (13.6%) exceeds the
share of plots with decreasing defoliation (12.4%). Plots showing deterioration are scattered
across Europe, but their share is particularly high in southern France, at the eastern edge of
the Pyrenean Mountains, in the Czech Republic, and in north eastern Italy.
35
Figure 3.1.3.1-3: Development of mean plot defoliation (slope of linear regression) of all species over the years
2002 – 2011.
36
3.1.3.2.
Pinus sylvestris
Pinus sylvestris is the most common tree Table 3.1.3.2-1: Shares of trees in different defoliation classes.
species in Europe. The area of its occurN Trees
0-10%
>10-25%
>25%
1992
19091
29.3
37.3
33.5
rence spreads from Scandinavia to the
1993
19150
28.8
38.9
32.3
Mediterranean region. When considering
1994
18450
27.7
37.9
34.3
the time series from 1992 on, the mean
1995
20649
33.7
38.1
28.2
defoliation has decreased with exception of
1996
20697
35.4
41.6
22.9
1997
20734
34.7
43.8
21.6
the recent years showing no change in
1998
21104
35.9
45.4
18.7
crown condition. In both time periods the
1999
21365
36.1
46.7
17.2
percentage of healthy pines (0-10%) in2000
21356
34.9
47.8
17.3
2001
21487
33.9
49.3
16.8
creased and the share of damaged trees
2002
21405
31.8
50.3
17.9
(>25%) decreased (Tab. 3.1.3.2-1, Fig.
2003
21405
30.8
51.4
17.8
3.1.3.2-1, Fig. 3.1.3.2-2). As regards the
2004
23116
34.4
47.8
17.8
last year in the time series a pronounced
2005
23338
35.4
46.3
18.3
2006
20755
38.6
45.8
15.6
increase of the share of trees damaged and
2007
21349
36.1
48.7
15.2
an increase of mean defoliation were no2008
19842
35.3
48.8
15.9
ticed.
2009
19133
35.3
47.7
17.1
Considered spatially (Fig. 3.1.3.2-3), the
only region showing a high share of deteriorated plots lies in the western part of the
Czech Republic. For most plots no signs of
positive or negative trends in the development of crown condition can be seen. The
share of pines exhibiting deteriorated
crown development since 2002 (15.2%)
exceeds the positive trend (9.9%).
2010
2011
19395
17124
35.2
29.2
48.2
50.8
16.6
19.9
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
29838
30196
30148
29855
29967
29798
30077
31593
31721
28990
29570
28046
27662
28001
25605
0-10%
27.7
29.2
30.6
30.2
30.4
32.0
31.6
35.2
35.5
37.4
34.8
32.5
32.6
33.0
29.2
>10-25%
44.6
45.8
47.6
49.9
51.3
51.6
52.0
48.3
47.6
48.1
50.9
52.7
52.0
51.9
54.0
>25%
27.7
25.0
21.8
19.9
18.3
16.4
16.5
16.6
16.9
14.6
14.2
14.8
15.4
15.1
16.8
80
30
70
60
20
15
10
1992-2011
1997-2011
5
percent of trees
25
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.2-1: M ean defoliation in two periods
(1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.2-2: Shares of trees of defoliation 0-10% and
> 25% in two periods (19922011 and 1997-2011)
37
Figure 3.1.3.2-3: Development of mean plot defoliation (slope of linear regression) of Pinus sylvestris over the
years 2002-2011
38
3.1.3.3.
Picea abies
Table 3.1.3.3-1: Shares of trees in different defoliation classes
Picea abies is the second most freN Trees
0-10%
>10-25%
>25%
1992
14379
30.6
36.3
33.1
quently occurring species in the large
1993
14519
31.4
36.1
32.4
scale tree sample. Its area extends
1994
14727
29.5
34.7
35.8
mainly from Scandinavia to northern
1995
16374
30.8
33.1
36.1
Italy.
The crown condition of Picea abies
slightly improved over both observation
periods. In summer 2003 extreme
weather conditions caused the mean
defoliation to peak in this year. Until
2006 a recuperation phase was observed. Since then the level of the crown
condition remained more or less unchanged (Tab. 3.1.3.3-1, Fig. 3.1.3.3-1,
Fig. 3.1.3.3-2).
Since 2006 the share of healthy trees (010%) increased permanently. Significant improvements in the crown condition of spruce were observed in 2001
and 2010.
Between 2002 and 2011 no clear trend
was observed for about 71% plots. In
this time period a deterioration of crown
condition occurred on 20.9% of the
plots. The share of the plots exhibiting
positive trend in crown condition between 2002 and 2011 is 8% (Fig.
3.1.3.3-3).
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
16222
16028
15513
15898
15888
15711
15784
15827
16361
16062
14143
13830
13605
13282
13780
12667
31.4
29.1
33.9
35.0
33.5
33.0
32.1
31.9
31.4
32.6
38.7
36.5
37.4
37.4
39.4
39.8
31.6
33.7
35.7
36.2
37.4
38.4
38.3
39.2
36.7
38.0
34.8
36.2
35.5
35.7
35.2
33.9
37.0
37.3
30.4
28.8
29.1
28.6
29.6
28.9
32.0
29.4
26.5
27.3
27.2
26.8
25.4
26.3
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
17919
17402
17799
17770
17511
17567
17673
18210
17708
15803
15496
15258
15207
15708
14603
0-10%
29.9
34.0
35.1
33.1
32.6
33.2
32.6
32.7
33.8
39.2
37.2
37.3
37.9
40.1
39.7
>10-25%
34.2
36.2
36.7
38.3
39.5
39.1
40.3
37.4
38.6
36.3
37.5
37.3
37.5
36.5
36.2
>25%
35.8
29.9
28.2
28.6
27.9
27.7
27.1
29.9
27.6
24.5
25.3
25.3
24.6
23.4
24.1
80
30
70
60
20
15
10
1992-2011
1997-2011
5
percent of trees
25
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.3-1: Mean defoliation in two periods
(1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.3-2: Shares of trees of defoliation 0-10% and > 25%
in two periods (1992-2011 and 1997-2011)
39
Figure 3.1.3.3-3: Development of mean plot defoliation (slope of linear regression) of Picea abies over the years
2002-2011
40
3.1.3.4.
Mediterranean
Table 3.1.3.4-1: Shares of trees in different defoliation classes.
N Trees
0-10%
>10-25%
>25%
lowland pines
1992
3866
63.9
24.3
11.8
To the group of Mediterranean
1993
3891
60.3
27.1
12.6
lowland pines belong P. pinas1994
3802
50.3
32.7
17.0
ter, P. halepensis and P. pinea.
1995
3823
39.2
43.8
17.0
1996
3815
36.6
45.4
17.9
Crown condition of these tree
1997
3769
40.3
48.3
11.5
species is characterized by a
1998
3827
37.1
47.3
15.6
pronounced increase in mean
1999
5202
40.8
47.6
11.6
defoliation since 1992. This is
2000
5279
39.1
48.6
12.2
evident from the development
2001
5287
34.0
54.6
11.5
of healthy trees. Their share
2002
5280
29.6
55.8
14.7
2003
5215
27.3
56.6
16.1
dropped from about 64% in
2004
5235
28.7
55.2
16.1
1992 to 27.9% in 2011. The
2005
5198
20.7
56.0
23.3
lowest share of undamaged trees
2006
5201
21.3
56.6
22.1
(18.1%) was recorded in 2009.
2007
5240
22.9
57.0
20.1
In contrast to the healthy trees
2008
5248
21.2
60.5
18.3
the percentage of damaged
2009
5105
18.1
61.0
20.8
2010
5085
23.2
58.7
18.1
pines peaked in 2005, decreased
2011
5084
27.9
56.0
16.2
thereafter and fluctuated since
then reaching about 16% in
N Trees
0-10%
>10-25%
>25%
2011 (Tab. 3.1.3.4-1, Fig.
1997
3944
38.5
46.4
15.1
3.1.3.4-1 and Fig. 3.1.3.4-2).
1998
3940
37.5
46.5
16.0
As regards the spatial trend, the
share of plots showing recovery
(14.7%) exceeds the share of
plots, on which the mean defoliation increased between 2002
and 2011 (11.7%). These plots
are mainly located along the
Mediterranean coast in France
and in northern Spain (Fig.
3.1.3.4-3).
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
5314
5368
5376
5345
5280
5348
5289
5290
5305
5313
5170
5150
5245
70
25
60
20
15
1992-2011
1997-2011
5
percent of trees
47.6
48.6
54.3
55.5
56.2
54.7
55.3
55.8
56.6
60.2
60.5
58.2
55.4
12.3
12.8
12.2
15.2
16.8
17.3
24.3
23.1
20.7
18.8
21.6
18.7
16.4
80
30
10
40.1
38.6
33.5
29.3
27.0
28.1
20.4
21.0
22.6
21.0
17.9
23.1
28.1
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.4-1: Mean defoliation in two
periods (1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.4-2: Shares of trees of defoliation 0-10% and >
25% in two periods (1992-2011 and 1997-2011)
41
Figure 3.1.3.4-3: Development of mean plot defoliation (slope of linear regression) of Mediterranean lowland
pines over the years 2002-2011
42
3.1.3.5.
Fagus sylvatica
N Trees
0-10%
>10-25%
>25%
Fagus sylvatica is the most frequent
1992
6254
43.7
35.5
20.8
deciduous tree species on the large1993
6368
45.1
34.7
20.2
scale plots. The area of its occur1994
6401
41.7
37.3
21.0
1995
6480
35.2
38.7
26.1
rence spreads from southern Scandi1996
6458
33.1
45.4
21.4
navia to Sicily and from the northern
1997
6309
29.7
46.9
23.4
1998
6588
32.9
45.1
22.0
coast of Spain to Bulgaria.
Since the beginning of the study
crown condition of this tree species
expressed by mean defoliation worsened slightly. The highest defoliation
was recorded in the year after the hot
and dry summer in central Europe in
2003. Since then a recovery has been
observed (Tab. 3.1.3.5-1, Fig.
3.1.3.5-1, Fig. 3.1.3.5-2)
Between 1992 and 2004 the percentage of healthy trees (0-10%) diminished from 43.7 to 18.3%. In 2011
the share of damaged trees (>25%)
rose rapidly reaching 34.5% in the
long and 29% in the short time series. But this is not reflected in the
shares of plots with decreasing
(8.1%) and increasing defoliation
(10.5%) (Fig. 3.1.3.5-3).
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
7244
7266
7328
7337
7299
7386
7448
6940
7106
7128
6985
7305
7316
26.2
29.6
25.3
26.3
23.7
18.3
24.0
26.4
23.2
29.1
24.8
26.6
22.6
49.5
46.7
48.0
50.4
50.2
47.3
47.7
44.9
50.6
49.1
44.2
47.8
42.9
24.3
23.7
26.7
23.3
26.1
34.4
28.3
28.7
26.2
21.8
31.0
25.6
34.5
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
7792
8176
8454
8668
8664
8772
8666
8613
8760
8315
8577
8533
9041
9327
9318
0-10%
33.1
35.6
30.7
33.9
29.3
30.3
28.1
21.9
28.6
30.3
28.4
32.8
32.6
32.3
30.3
>10-25%
44.5
43.3
46.9
44.0
45.4
47.5
48.4
47.3
45.9
43.4
48.1
47.6
42.2
45.6
40.8
>25%
22.4
21.0
22.4
22.1
25.4
22.1
23.5
30.8
25.5
26.3
23.5
19.6
25.2
22.1
29.0
80
30
70
60
20
15
10
1992-2011
1997-2011
5
percent of trees
25
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
(1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
43
Figure 3.1.3.5-3: Development of mean plot defoliation (slope of linear regression) of Fagus sylvatica over the
years 2002-2011
44
3.1.3.6.
The group of deciduous temperate oaks
includes Quercus robur and Q. petraea
occurring throughout central Europe.
Table 3.1.3.6-1: Shares of trees in different defoliation
classes.
Temporal development of these tree species is characterized by peaks in 1997
and 2005. In the subsequent years some
trees may have recovered as the percentage of trees defoliated by 25% and more
decreased and varied since then between
33% and 37% in both time series (Tab.
3.1.3.6-1, Fig. 3.1.3.6-1, Fig. 3.1.3.6-2).
The linear trend based on time period
2002-2011 shows that on only 7.7% of
all plots the mean defoliation increased.
For 75.8% of the plots the calculation
does not support the existence of any
trends.
A cluster of plots showing decreasing
trend of mean defoliation can be seen in
central France (Fig. 3.1.3.6-3).
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
5312
5393
5608
5464
5434
5447
5601
5720
5749
5751
5763
5766
5869
5880
5388
5490
5668
5605
5666
5803
0-10%
42.3
36.8
34.0
32.9
24.6
16.3
20.5
20.4
21.0
18.8
18.1
14.5
14.7
13.3
16.8
15.6
15.8
17.9
16.1
17.1
>10-25%
35.2
33.1
31.9
36.5
39.1
42.7
42.6
47.9
48.4
49.7
51.0
47.3
44.7
43.6
46.2
47.1
48.0
46.6
47.6
47.3
>25%
22.5
30.1
34.1
30.6
36.3
41.0
36.9
31.7
30.6
31.5
30.8
38.2
40.6
43.1
37.0
37.3
36.2
35.5
36.3
35.6
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
6548
6760
6791
6882
6811
6654
6659
6780
6849
6348
6475
6642
6928
6917
7110
0-10%
16.5
20.1
21.0
20.2
18.9
18.8
15.3
16.2
14.6
19.2
17.5
17.2
19.3
17.7
18.3
>10-25%
41.9
41.6
47.4
46.6
48.4
50.8
47.6
44.5
43.5
45.6
47.6
48.8
48.1
47.5
47.9
>25%
41.6
38.3
31.6
33.2
32.6
30.4
37.1
39.4
41.9
35.2
34.9
34.0
32.7
34.7
33.8
80
30
70
60
20
15
10
1992-2011
1997-2011
5
percent of trees
25
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
(1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.6-2: Shares of trees of defoliation 010% and > 25% in two periods (1992-2011and 19972011)
45
Figure 3.1.3.6-3: Development of mean plot defoliation (slope of linear regression) of deciduous temperate oak
species over the years 2002-2011
46
3.1.3.7.
Deciduous (sub-) Mediterra- Table 3.1.3.7-1: Shares of trees in different defolianean oak
tion classes.
N Trees
0-10%
>10-25%
>25%
The group of deciduous (sub-) Mediterra1992
3156
54.3
32.8
12.8
nean oak is composed of Quercus cerris,
1993
3154
53.0
31.8
15.2
1994
3123
49.5
32.8
17.7
Q. pubescens, Q. frainetto and Q.
1995
3170
47.4
34.9
17.7
pyrenaica. The occurrence of these oaks is
1996
3218
30.5
43.7
25.8
1997
3056
27.1
42.5
30.4
confined to southern Europe.
Crown condition of these oaks deteriorated
dramatically until the end of the 1990s. In
the following years the share of damaged
trees increased at slower rate reaching a
maximum of 36.4% in 2006. In the subsequent years a slow decrease in defoliation
can be observed (Tab. 3.1.3.7-1, Fig.
3.1.3.7-1 and Fig. 3.1.3.7-2). The share of
damaged trees in the last four years levelled off at about 33% (long time series) or
30% (short time series).
The spatial distribution shows a negative
trend in crown condition on 12.3% of all
plots mainly in southern France. Positive
development of the four oak species was
identified on 17.4% plots. These plots are
more or less scattered over the southern
Europe. A small cluster of plots with positive trend of crown condition since 2002
can be identified in Serbia (Fig. 3.1.3.7-3).
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
3084
3678
3648
3686
3599
3519
3625
3580
3583
3588
3606
3608
3967
3970
26.1
24.8
22.5
20.2
18.4
16.7
16.2
18.5
17.5
14.9
16.3
16.2
19.3
18.6
41.8
46.1
46.8
45.0
46.0
46.2
48.8
48.5
46.1
49.3
50.1
50.1
48.9
46.8
32.1
29.1
30.6
34.8
35.6
37.0
35.0
32.9
36.4
35.8
33.6
33.6
31.8
34.7
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
4037
4392
4628
4530
4704
4599
4376
4468
4409
4577
4387
4390
4832
5262
5335
0-10%
23.4
21.7
24.4
20.4
19.0
15.9
14.2
14.3
17.1
15.8
13.6
14.9
15.8
17.8
17.5
>10-25%
40.0
39.9
45.2
45.5
44.7
48.6
48.0
48.6
49.7
47.2
50.7
51.4
53.1
51.8
52.4
>25%
36.6
38.3
30.4
34.1
36.3
35.4
37.8
37.1
33.2
37.0
35.7
33.7
31.1
30.4
30.1
80
30
70
60
20
15
10
1992-2011
1997-2011
5
percent of trees
25
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.7-1:Mean defoliation in two periods
(1992-2011 and 1997-2011)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.7-2: Shares of trees of defoliation 010% and > 25% in two periods (1992-2011 and
1997-2011)
47
Figure 3.1.3.7-3: Development of mean plot defoliation (slope of linear regression) of deciduous (sub-) Mediterranean oak species over the years 2002-2011
48
3.1.3.8.
Evergreen oak
N Trees
0-10%
>10-25%
>25%
The group of evergreen oaks in1992
3362
47.4
44.4
8.2
cludes Quercus coccifera, Q. ilex,
1993
3315
41.5
51.0
7.5
Q. rotundifolia and Q. suber. As the
1994
3288
31.4
52.4
16.2
1995
3329
19.2
48.5
32.3
compositions of countries on which
1996
3307
18.1
53.6
28.4
both time series are based do not
1997
3306
22.3
58.1
19.6
substantially differ the results pre1998
3264
28.6
56.0
15.4
1999
4232
21.7
57.0
21.3
sented in Table 3.1.3.8-1 are very
2000
4308
19.3
60.4
20.4
similar.
2001
4324
19.9
62.6
17.5
In the early 1990s, at the beginning
of the study, the mean defoliation of
evergreen oaks was less than 15%,
which corresponds with a high percentage of healthy trees. The share
of damaged trees (> 25%) shows
three peaks: in 1995 (32.3%), in
2005 (27.9%) and in 2006 (27.3%)
(Tab. 3.1.3.8-1).
The majority of plots with evergreen
oaks are located in Spain. Few of
the plots are in southern France and
along the western coast of Italy. The
share of evergreen oaks with deteriorating trends between 2002 and
2011 is with 8.1% rather small. The
share of plots on which these oak
species have been recovering since
2002 is by 10% point higher (Fig.
3.1.3.8-3).
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
4311
4218
4280
4229
4233
4318
4336
4345
4446
4473
16.2
14.0
17.7
9.8
8.8
10.1
11.6
11.0
17.3
19.7
62.8
62.3
63.5
62.3
63.9
67.5
67.2
67.0
62.2
62.2
21.0
23.6
18.8
27.9
27.3
22.5
21.2
22.0
20.5
18.1
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
N Trees
3354
3288
4256
4332
4348
4335
4242
4328
4277
4281
4366
4360
4369
4494
4545
0-10%
22.1
28.4
21.6
19.2
19.8
16.1
14.0
17.5
9.8
8.8
10.3
11.9
11.3
17.4
19.8
>10-25%
57.7
56.1
57.1
60.2
62.7
63.0
62.5
63.8
62.3
63.8
67.3
67.0
66.8
61.9
61.8
>25%
20.2
15.5
21.2
20.6
17.4
20.9
23.5
18.6
27.9
27.4
22.4
21.1
21.9
20.8
18.3
80
30
70
60
20
percent of trees
25
15
10
1992-2011
1997-2011
5
50
0-10% (92-11)
>25% (92-11)
0-10% (97-11)
>25% (97-11)
40
30
20
10
0
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
year
Figure 3.1.3.8-1: Mean defoliation in two periods (1992-2011 and 1997-2011)
1993
1995
1997
1999
2001 2003
year
2005
2007
2009
2011
49
Figure 3.1.3.8-3: Development of mean plot defoliation (slope of linear regression) of evergreen oak species
over the years 2002-2011
50
3.2. Damage cause assessment
Crown condition is the most widely applied indicator for forest health and vitality in Europe.
In order to interpret the crown condition accurately, it is necessary to assess tree parameters
that have an influence on tree vitality. Parameters assessed in addition to crown condition
include discolouration and damages caused by biotic and abiotic factors. Through the assessment of damage and its influence on crown condition, it is possible to draw conclusions on
cause-effect mechanisms. Since 2005, tree crowns on Level I plots have been examined based
on an amended method for damage assessment, which allows to obtain more information on
injury symptoms, possible causes of damage, and extent of the injury.
The aim of the damage cause assessment is to collect as much information as possible on the
causal background of tree damages in order to enable a differential diagnosis and to better
interpret the unspecific parameter ‘defoliation’.
3.2.1. Background of the survey in 2011
Assessment of damage causes is part of the visual assessment of crown condition. All trees
included in the crown condition sample (Level I plots) are required to be regularly assessed
for damage causes. In 2011, damage causes were assessed on about 6 000 plots in 28 countries across Europe (Fig. 3.2.1-1). The number of trees showing damage was 44 427. As a
particular tree may be affected by more than one damage agent the total number of damage
cases recorded was 59 014.
Figure 3.2.1-1: Plots with damage cause assessment in 2011
51
52
3.2.2. Assessment parameters
The assessment of damage to trees based on the ICP Forests methodology includes three
steps: symptom description, determination of causes, and quantification of the symptoms.
Several symptoms of damage can be described for each tree. The symptom description should
focus on important factors which may influence crown condition.
Symptoms
Symptom description aims at describing visible damage causes for
single trees. The description indicates affected parts of the assessed
trees and type of symptoms observed.
Symptom
description
should focus on important factors
that may influence the crown condition.
Table 3.2.2-1: Affected parts of a tree
Three main categories are distinguished for indicating the affected
part
of
each
tree:
(a)
leaves/needles,
(b)
branches,
shoots, & buds, and (c) stem &
collar. For each affected tree area,
further specification is required
(Tab. 3.2.2-1). Symptoms are grouped into broad categories like wounds, deformations, necrosis etc. This allows a detailed description of the occurring symptoms.
Table 3.2.2.-2: Damage extent classes
Extent
The damage extent is classified in eight classes
(Tab. 3.2.2-2) In Trees where multiple damages
occurred (and thus multiple extent classes), only
the highest value was evaluated.
Causal agents
For each symptom description a causal agent must
be determined. The determination of the causal
agent is crucial for the study of the cause-effect
mechanism. Causal agents are grouped into nine
categories (Tab. 3.2.2-3). In each category a more
detailed description is possible through a hierarchical coding system.
Class
05
1 – 10 %
11 – 20 %
21 – 40 %
41 – 60 %
61 – 80 %
81 – 99 %
100 %
Table 3.2.2-3: Main causal agents
Agent group
Game and grazing
Insects
Fungi
Abiotic agents
Direct action of men
Fire
Atmospheric pollutants
Other factors
(investigated but) unidentified
53
3.2.3. Results in 2011
3.2.3.1.
Agent groups
The distribution of the agent groups in 2011 shows that over 15 000 trees displayed symptoms
caused by insects (Fig. 3.2.3.1-1) corresponding to 32% of the records (Tab. 3.2.3.1-1).
Roughly half of the insect-caused symptoms were attributed to defoliators and to the other
half to borers and other insects. Significantly fewer trees, namely over 11 000, displayed
damage caused by fungi. In about 6 000 trees, an abiotic symptom (i.e. drought, frost) was
found. Altogether, ca. 18 000 trees showed no signs of damage. Multiple agent groups were
recorded for a number of trees. The damages due to air pollution refer to “direct smoke damages”, indirect effects were not assessed.
Investigated but unidentified
Others
Other factors
drougt
frost
Atmospheric pollutants
Canker
Fire
Decay and root rot
Needle casts and needle rust
Defoliatiors
Abiotic agents
Borers
Fungi
Mining insects
Insects
Game and grazing
number of trees
0
5000
Figure 3.2.3.1-1: Frequency of agent groups
10000
15000
20000
54
share of damages
by agent group and
country for the
year 2011
Game and grazing
Insects
Fungi
Abiotic agents
Fire
Atmospheric
pollutants
Other factors
Investigated but
unidentified
Table 3.2.3.1-1: Percentages of damage types by agent group and country for the year 2011
Belgium
Bulgaria
Cyprus
Czech Rep.
Denmark
Estonia
Finland
France
Germany
Hungary
Italy
Latvia
Lithuania
Poland
Romania
Slovenia
Spain
Sweden
EU
Andorra
Belarus
Montenegro
Norway
Russian Fed.
Serbia
Switzerland
Turkey
Total Europe
1
0
1
14
6
2
1
1
3
0
1
18
6
1
3
0
1
4
1
0
1
0
1
0
0
0
0
1
9
30
73
1
62
8
16
68
32
29
25
8
5
24
29
24
32
2
28
13
10
26
27
12
22
0
32
26
12
31
0
0
5
43
19
22
7
17
8
13
14
8
9
14
14
9
14
61
41
10
37
26
12
0
4
15
12
5
19
73
18
6
7
0
7
25
4
19
25
5
23
7
25
5
11
13
5
5
13
19
4
0
6
11
8
9
0
3
3
8
6
0
11
9
0
28
19
7
6
8
5
21
5
0
23
6
1
9
0
0
1
6
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
4
0
1
0
1
2
0
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
7
4
1
2
15
9
6
10
6
6
4
19
4
4
13
1
8
0
12
1
3
10
2
0
24
9
58
24
0
5
5
31
36
0
34
9
56
4
27
36
26
43
6
58
32
13
6
50
18
22
60
100
33
31
55
Agent Group ‘Game and grazing’
In 2011, only minor damage from ‘game and grazing’ was observed on the assessed trees
throughout Europe. Tab. 3.2.3.1-1 displays that 1% of all recorded damages were caused by
this agent group. It has however to be taken into account that only adult trees in KRAFT classes 1-3 are regularly assessed for damage types and browsing in the herb and shrub layer is
not recorded in this assessment. 78.0% of all affected plots show a share of damaged trees of
25% or lower (Fig. 3.2.3.1-2).
Figure 3.2.3.1-2: Shares of trees per plot with recorded agent group ‘game and grazing’, 2011
56
Agent Group ‘Insects’
‘Insects’ were the most frequently detected agent group (32% of all damage types) in 2011.
They were observed in different intensities throughout Europe. On around half of all affected
plots, more than 25% of the trees were damaged by insects. Plots with over 75% of the trees
affected account for nearly 15% of all plots. They are clustered e.g. at the eastern edge of the
Pyrenean Mountains, Italy and Cyprus (Fig. 3.2.3.1-3).
Figure 3.2.3.1-3: Shares of trees per plot with recorded agent group ‘insects’, 2011
57
Agent Group ‘Fungi’
Most affected plots (71.2%) showed only a small share of damaged trees. On 6.7% of all affected plots, between 50 and 75% of the trees showed damage caused by fungi, and on 5.8%
of all plots more than 75% of the trees were damaged. A particularly high share of plots damaged by fungi was found in Estonia and western Bulgaria (Fig. 3.2.3.1-4).
Figure 3.2.3.1-4: Shares of trees per plot with recorded agent group ‘fungi’, 2011
58
Agent Group ‘Abiotic agents’
In 2011, the share of trees with damage caused by “abiotic agents” was 6%. The most frequent causes were drought, frost/snow, and wind. 79% of all affected plots showed a small
share of damaged trees. Plots with a higher share of damaged trees were found mainly in
southern Europe. In particular, these plots occurred at the eastern edge of the Pyrenean Mountains (Fig. 3.2.3.1-5).
Figure 3.2.3.1-5: Shares of trees per plot with recorded agent group ‘abiotic agent’, 2011
59
3.3. Methods of the national surveys
National surveys are conducted in many countries in addition to the transnational surveys.
The national surveys in most cases rely on denser national grids and aim at the documentation
of forest condition and its development in the respective country. Since 1986, densities of
national grids with resolutions between 1 x 1 km and 32 x 32 km have been applied due to
differences in the size of forest area, in the structure of forests and in forest policies.
Results of crown condition assessments on the national grids are presented in Chapter 6 and
Annex II. Comparisons between the national surveys of different countries should be made
with great care because of differences in species composition, site conditions and methods
applied.
3.4. References
Chappelka, A.H., Freer-Smith, P.H. (1995): Predeposition of trees by air pollutants to low
temperatures and moisture stress. Environmental Pollution 87: 105-117.
Cronan, C.S., Grigal, D.F. (1995): Use of calcium/aluminium ratios as indicators of stress in
forest ecosystems. Journal of Environmental Quality 24: 209-226.
EEA (2007): European forest types. Categories and types for sustainable forest management
reporting and policy. European Environment Agency (EEA) Technical Report 9/2006,
2nd edition, May 2007, 111 pp. ISBN 978-92-9167-926-3, Copenhagen.
Freer-Smith, P.H. (1998): Do pollutant-related forest declines threaten the sustainability of
forests. Ambio 27: 123-131.
ICP Forests (2010). Manual on methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests. UNECE, ICP Forests,
Hamburg. ISBN: 978-3-926301-03-1. [http://www.icp-forests.org/Manual.htm]
Lorenz, M., Becher, G. (1994): Forest Condition in Europe. 1994 Technical Report. UNECE
and EC, Geneva and Brussels, 174 pp.
Requardt A., Schuck A., Köhl M. (2009). Means of combating forest dieback - EU support
for maintaining forest health and vitality. iForest 2: 38-42. [online 2009-01-21] URL:
http://www.sisef.it/iforest/contents/?ifor0480-002
60
4. Sulphate and nitrogen deposition and trend analyses
Peter Waldner, Anne Thimonier, Maria Schmitt (ch), Aldo Marchetto, Michela Rogora (it), Oliver Granke,
Volker Mues (pcc), Karin Hansen, Gunilla Pihl-Karlsson (se), Daniel Zlindra (si), Nicolas Clarke (no), Arne
Verstraeten (be), Andis Lazdins (lv), Claus Schimming (de), Carmen Iacoban (ro), Antti-Jussi Lindroos (fi),
Elena Vanguelova, Sue Benham (uk), Henning Meesenburg (de), Manuel Nicholas (fr), Anna Kowalska (pl),
Vladislav Apuhtin, Ulle Nappa (ee), Zora Lachmanová (cz), Markus Neumann (at), Albert Bleeker (nl), Morten
Ingerslev (dk), Juan Molina (es), Lars Vesterdal (dk), Walter Seidling, Uwe Fischer (de), Richard Fischer and
Martin Lorenz1.
4.1. Introduction
The atmospheric deposition of sulphur (S) and nitrogen (N) compounds affects forest ecosystems through several processes. Deposition of acidifying compounds, inorganic nitrogen as a
nutrient and base cations to forests in Europe is a major driver for many processes in forests.
The development of deposition is of high interest and therefore, trend analyses of ICP forests
deposition data are regularly produced and published (e.g. Lorenz & Granke, 2009). However,
until recently, these trend analyses were usually carried out with data covering the last six
years only. Trend analyses with longer time series using linear regression techniques have
first been included in the Technical Report 2010 (Granke & Mues, 2010). Trend analyses of
part of the ICP Forests deposition data have also been carried out at the national level using
Mann Kendall tests or autoregressive time series modelling (e.g. Meesenburg et al., 1995;
Kvaalen et al., 2002; Moffat et al., 2002; Lange et al., 2006; Rogora et al., 2006; Wu et al.,
2010b; Graf Pannatier et al., 2011).
The various available techniques have their advantages and disadvantages, and the detection
and magnitude of trends may to some extent depend on the test used. For example, the linear
regression test cannot distinguish between trends caused by changes in precipitation volume
and trends caused by changes in the ‘pollution climate’. In comparison, non-parametric tests
such as Seasonal Mann Kendall tests are more robust against sporadic events, such as high
calcium (Ca) peaks caused by Saharan dust events. Secondly, the minimal detectable trend
may depend on the uncertainties included in the deposition measurements (ICP-Forests Manual, ICP-Forests, 2010).
When multiple tests are carried out on a large data set, the possible effect of the size of the
data set needs to be considered. For example, if hundreds of tests are carried out on the basis
of test having a probability threshold of 0.01, the probability of some false positive becomes
relevant. On the other hand, even non-significant trends may indicate a significant change,
when the trends have the same direction for most of a large number of trends.
4.2. Objectives
The main goal of this study is to detect trends in deposition at ICP Forests Level II sites (with
ICP Forests and pre-ICP Forests data) and to investigate possible causes. The specific objectives are to:

1
determine the bulk and throughfall deposition of sulphate and inorganic nitrogen (nitrate and ammonium) and its trends
For addresses see Annex III-4
61

investigate the influence of the trend analyses technique on the detection of statistically significant trends by comparing the linear regression test with the Seasonal
Mann-Kendall approach.

investigate the minimal detectable trend in case of deposition measurements made according to the ICP Forests Manual.

investigate and discuss possible reasons for trends on a European and a regional level.
4.3. Methods
Continuous sampling of throughfall (TF) and bulk deposition (BD) is carried out on ICP Forests intensive monitoring plots (Level II) and at a nearby open field, respectively. The methods used in the countries fulfil the requirements defined in the ICP-Forest Manual (earlier
versions and ICP-Forests, 2010) to a large extent (Norway: Kvaalen et al., 2002; Moffat et al.,
2002; Italy: Mosello et al., 2002; Switzerland: Thimonier et al., 2005; Czech Republic: Boháčová et al., 2010; UK: Vanguelova et al., 2010; Wu et al., 2010a; Swedish Throughfall
Monitoring Network (SWETHRO): Pihl Karlsson et al., 2011). Collectors (10 to 20 replicates) are placed in the forest based on a random or fixed design in order to cover the spatial
variation. Tests to determine the minimal number of samplers required to cover spatial variations to gain a representative plot mean have been carried out on a number of plots (e.g. Thimonier, 1998; UK: Houston et al., 2002; Belgium: Staelens et al., 2006). Some samples are
collected at least monthly, filtered, and then stored at about 4°C before chemical analyses are
performed to determine the concentrations of the macronutrients. The laboratory results are
checked for internal consistency based on the conductivity, the ion balance, the concentration
of organic N and the Na/Cl ratio, and are repeated if suspicious. The QA/QC procedures further include the use of control charts for internal reference material to check long-term comparability within national laboratories as well as participation in periodic working ring tests
(e.g., Marchetto et al., 2006) to check international comparability.
Data was submitted annually by countries to the Programme Coordinating Centre (PCC),
checked for consistency and stored in ICP Forest database.
We selected the data used in the analysis by applying the following criteria to the deposition
data from the years 1998 to 2010: (i) continuous sampling during >330 days per year, (ii)
non-missing concentration values for >330 days per year. Hereby, sampling periods with
mean precipitation below 0.1 mm days-1 were counted as non-missing even if no chemical
analyses could be performed.
Since precise dates of the sampling periods have not been submitted for data collected before
2007, the sampling dates were reconstructed based on start and end date and the number of
sampling periods per year. Data of the sampling periods were interpolated to regular monthly
and annual data with three steps: (i) intersection of sampling periods at end of months/years
distributing precipitation quantity proportional to the duration to the new sampling periods,
/split of every sampling period overlapping two consecutive months into two new sampling
periods, by distributing precipitation quantity in proportion to the duration of the new sampling periods (ii) using deposition=0 for periods with missing concentrations and mean precipitation < 0.1 mm day-1 (iii) calculation of the deposition fluxes qc (kg ha-1 a-1) of these periods by multiplying precipitation quantity q (L m-2) with the concentrations c (mg L-1) with
qc = 0.01 q c
and summing up the fluxes of months and years.
62
Both bulk precipitation and throughfall deposition of sulphur were corrected for the contribution from sea salt to estimate the anthropogenic part of sulphur deposition SO4--corr (mg L-1)
with
SO4--corr = SO4-- - 0.54 Clwhere SO4-- and Cl- (mg L-1) are the concentration of sulphate and chloride.
Trend analyses were carried out with (i) linear regression (LRegr) (Granke & Mues, 2010)
and (ii) Mann-Kendall (MK) test (Mann, 1945; Helsel & Hirsch, 2002) using annual deposition fluxes, and with (iii) Seasonal Mann-Kendall (SK) (Hirsch et al., 1982; Hirsch & Slack,
1984), and (iv) Partial Mann Kendall (PMK) tests (Libiseller & Grimvall, 2002) using month
ly deposition data. The SAS and R software was used for the linear regression and Kendall
tests, respectively (Marchetto, 2012). For the Kendall tests (MK, SK, PMK), trend slopes
were estimated using Sen’s (1968) equations.
We calculated a relative slope rslope (a-1), an estimated mean relative change per year, with
rslope = slope / mean,
where slope (kg ha-1 a-1/a) is the estimator for the absolute trend resulting from the trend
analyses and mean (kg ha-1 a-1) is the mean value of the time series.
The relative slope was plotted against the p-value to investigate patterns that may be used to
define a minimal detectable trend for deposition data.
4.4. Results
4.4.1. Current deposition
Mean annual throughfall (TF) and bulk deposition (BD) of sulphur and nitrogen was calculated for 289 and 357 plots with at least one of the years 2008, 2009 and 2010 meeting the
mentioned completeness criteria (Figures 4.4.1-1 and 4.4.1-2).
High sulphur deposition has been measured in northern central Europe especially in a region
covering Belgium/Netherlands, Central Germany, Czech Republic and Poland, as already
mentioned by Granke et al. (2010), reaching up to the southern Baltic and the Central Hungarian area. Furthermore, high values have also been found in some Mediterranean regions in
Spain, France, Southern Italy and Greece. Higher values in throughfall than bulk deposition
confirm that sulphur is filtered from the air by the tree canopies. High sulphur depositions
along the coast mostly occur with high Cl deposition, which is typical for sulphur that originates from sea salt.
63
Figure 4.4.1-1 Mean annual sulphate sulphur (SO4--) throughfall and bulk deposition 2008 to 2010 with and
without sea salt deposition included. Corr = no sea salt included
64
Figure 4.4.1-2 Mean annual nitrate (NO3—N) and ammonium (NH4+-N) bulk and throughfall deposition during
the period from 2008 to 2010.
65
High nitrogen deposition is also recorded in northern central Europe, as for sulphur, but extends further to the South down to southern Germany and the Swiss Plain, as observed in earlier years (Granke & Mues, 2010). Data from sites in UK and Ireland, not included in Granke
et al. (2010), show that for ammonia the high deposition regions extends also further to the
West, not only to northern France, but also to central UK and Ireland. In contrast to sulphur,
the regions south of the Alps show relatively high bulk and throughfall deposition of nitrate
and ammonium as well. In the Mediterranean area, relatively high values have been recorded
at some sites in Spain and in southern France.
4.4.2. Temporal trends
For 87 and 55 sites with throughfall and bulk deposition measurements from 1998 to 2001
respectively, we calculated time series for sulphur and nitrogen. These time series include the
period 1998 to 2007 for which trend analyses have been carried out by Granke et al. (2010) as
well as the period from 2001 to2010 analysed here. At some few of these sites, the correction
for sea salt could not be performed because chloride data did not meet the mentioned criteria.
The sulfur deposition showed a decreasing trend for the period from 2001 to 2010 that is detected by linear regression as being significant for the majority of the sites (Figure 4.4.2-1)
Figure 4.4.2-1 Trend of sulphate (SO4-2–S) bulk and throughfall deposition on plots with continuous measurements from 2001 to 2010. Non-significant positive trends are indicated with ‘no change (+)’ and non-significant
negative trends with ‘no change (-)’.
The mean of the sites with continuous measurement from 1998 to 2010 decreased from about
10 and 7 kg S ha-1 a-1 to about 5 and 4 kg S ha-1 a-1 (Figure 4.4.2-3) for throughfall and bulk
deposition, respectively. This corresponds to a relative decrease of about 6% per year. However, for the individual sites, the mean relative decrease ranged from about 0% to 10% per
year (Figure 4.4.3-1) and low relative decreases have also been estimated for some of the sites
66
with high sulphur deposition. In comparison, the mean precipitation volume remained quite
stable except for the drier year 2003 and, to a lesser extent, 2005.
Figure 4.4.2-2: Trend of nitrate (NO3--N) and ammonium (NH4+-N) bulk and throughfall deposition of plots
with continuous measurements from 2001 to 2010. Non-significant positive trends are indicated with ‘no change
(+)’ and non-significant negative trends with ‘no change (-)’.
67
For the plots with continuous inorganic nitrogen deposition measurement in bulk and throughfall from 2001 to 2010, trends that were significant have been detected for less plots than for
sulphate (Figure 4.4.2-2) The mean throughfall deposition of inorganic nitrogen on sites with
continuous measurements from 1998 to 2010 decreased from about 11 to about 9 kg N ha-1 a-1
i.e. circa 20%, corresponding to a mean decrease of about 1 to 2% per year (Figure 4.4.2-4).
9000
BD SO4-S (n=55)
TF SO4-S (n=87)
BD SO4-S (corr) (n=53)
TF SO4-S (corr) (n=86)
BD quantity
TF quantity
10
8000
7000
6000
5000
4000
5
quantity [mm]
deposition [kg/ha]
15
3000
2000
1000
0
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0
2011
Figure 4.4.2-3: Mean sulphate (SO4---S) bulk and throughfall deposition and precipitation volume (quantity) on
plots with continuous measurements from 1998 to 2010, with and without correction for sea salt deposition. Corr
= no sea salt included. Error bar show the standard error of the mean.
9000
BD NH4-N (n=55)
TF NH4-N (n=87)
BD NO3-N (n=55)
TF NO3-N (n=87)
BD quantity
TF quantity
8000
7000
6000
5000
5
4000
quantity [mm]
deposition [kg/ha]
10
3000
2000
1000
0
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0
2011
Figure 4.4.2-4: Trend of nitrate (NO3--N) and ammonia (NH4+-N) bulk and throughfall deposition and precipitation volume (quantity) of plots with continuous measurements from 1998 to 2010. Error bars show the standard
error of the mean.
68
Figure 4.4.2-5: Recent trend of nitrate nitrogen (NO3--N) and ammonia nitrogen (NH4+-N) bulk and throughfall
deposition of plots with continuous measurements from 2005 to 2010.
69
4.4.3. Comparison of trend analyses techniques
The slope estimates of the Kendall trend analyses techniques showed a quite high agreement
to those of linear regression in Figure 4.4.3-1 (left side) for SO4--S fluxes in throughfall. The
agreement of slopes was highest for Mann-Kendall (MK) that was performed with annual data
as well. It was a bit less high for Seasonal Mann-Kendall (SK) and Partial Mann-Kendall
(PMK) tests that were carried out with monthly data and aim on taking into account seasonal
variation and the co-variable precipitation quantity. Significance of a statistical test is given, if
the p-value is lower than a certain value, e.g. p<0.05 in case of a 95% significance level. In
Fig. 4.4.3-1 (right side) the p-values of the Kendall tests are plotted against those of the linear
regression (LR) than the slopes test. In general, the differences were lower for longer time
series. The relation between the p-values of MK and p-values of LR is not very strong, the
points scatter. The p-values of SK and especially PMK however, tend to be lower than those
of LR.
Figure 4.4.3-1: Relative slope (rslope) and p-value of Kendall trends tests MK, SMK and PMK versus linear
regression trend test for SO4---S throughfall deposition from 1998 to 2010 (12 years), 2001 to 2010 (10 years),
and 2007 to 2010 (4 years).
70
The compared trend analyses techniques resulted thus in similar trend slope estimates, but
detection with statistical significance was more frequent for PMK and SK than for MK and
LReg.
4.4.4. Estimation of minimal detectable trend
The minimal relative slope of a monotonic trend that is required to identify this trend with
statistical significance was investigated by plotting the p-value of the trend test versus the
relative slope estimate (rslope).
Fig.4.4.4-1 shows the p-values are plotted against the relative slope rslope for trend tests of
throughfall deposition of SO4---S time series with 4, 6, 9, 10 and 12 years of continuous
measurement until 2010.
Figure 4.4.4-1: P-value plotted against relative slope estimates of trend analyses with linear regression (LReg),
Mann-Kendall (MK) of annual means, Seasonal Mann-Kendall (SK) and Partial Mann-Kendall (PMK) of SO4--S throughfall (TF) deposition fluxes time series with 4, 6, 9, 10 and 12 years of continuous data until 2010.
On this p-value vs. rslope graph, most dots, i.e. trend estimates, are within a quite narrow
band with the shape of a Gaussian curve. This band and its Gaussian curve shape is narrower
for longer the time series and slightly differ from trend test to trend test. For linear regression
(LReg) of times series with 12 years of SO4--S throughfall data, most trends with rslope above
3% to 5% per year have p<0.05 (significant) and while most of trends with rslope below 3 to
5% have p>0.05 (not significant). We may conclude that an rslope of at least 3% to 5% per
year is required to detect a trend with statistical significance in this case. For shorter time series the required relative trend slopes rslopes seem to be higher while it seem to be lower for
the Seasonal Mann-Kendall (SK) and the Partial Mann-Kendall (PMK) test. The patterns were
similar for the major macro nutrients ammonia, nitrate, calcium and magnesia as shown in
71
Fig. 4.4.4-2 the minimal detectable trends seem to be slightly higher for elements with higher
temporal variability due to e.g. sporadic events such as Saharan dust deposition for calcium.
NH4+-N
Mg
Ca
NO3--N
Figure 4.4.4-2: p-value plotted against relative slope estimates of trend analyses with Seasonal Mann-Kendall
(SK) for ammonium, magnesium, calcium and nitrate throughfall (TF) deposition fluxes time series with continuous data from 1998 to 2010 (12 years).
4.5. Discussion
This joint evaluation that involved numerous national experts responsible for the deposition
measurements in their countries opened the opportunity (i) to reconstruct the sampling periods
for older datasets, (ii) to complete the dataset in case of missing years, (iii) to include corrections made on national level as well as to integrate arguments for the interpretation of the results.
The comparison of the trend analyses techniques confirmed that the choice of a specific
method has an influence on the number of trends identified as being significant. Seasonal
Mann-Kendall and Partial Mann-Kendall tests applied to monthly data identified more significant trends than linear regression techniques. However, there was a quite high agreement
between the slope estimates of the trend analyses techniques.
Minimal detectable trends were derived from a quite strong relation between trend significance and relative trend slope seem in the deposition fluxes time series of the same length.
The results were relatively consistent in terms of the minimal relative slope required for a
trend to be detected as significant for a certain length of time series. The minimal detectable
trend (mdT) seem to be a bit smaller for Partial Mann-Kendall tests than for the other tests.
The mdT was a bit higher for calcium (Ca) than for the other investigated nutrients. This
72
might be explained ba a higher temporal variability. For Ca often reported that a high part of
the annual deposition is due to relatively few peak events (e.g. Rogora et al., 2004).
For time series with 10 years the ‘minimal detectable trend’ estimated from the p-value vs.
rslope diagram is in the same order of magnitude as the data quality objective (DQO) for the
determination of the annual deposition defined in the ICP-Forests Manual (ICP-Forests,
2010). Working ring-tests for laboratory inter-comparison (Marchetto et al., 2011) as well as
the field inter-comparison exercise with (i) a common (harmonized) sampler on national plots
(Zlindra et al., 2011) as well as with (ii) national samplers in a common plot (Bleeker et al.,
2003; Erisman et al., 2003) showed that these data quality objectives are realistic and that it
can be assumed that they are met for the majority of the ions, laboratories and plots.
The trends found in this study for S and N compounds are in agreement with most other studies (Vanguelova et al., 2010; Graf Pannatier et al., 2011). Meesenburg et al. (1995) carried out
trend analyses of annual deposition data from 1981 to 1994 of 4 plot in Germany with linear
regression techniques. They also found significant decrease of SO4— with rslope between -5
and -9% a-1 for all plots but only a slight decreasing tendency for NO3- (between 0 and -3%
a-1) that was significant only at one plot. Kvaalen et al. (2002) also found a significant decreasing trend (rslope between +2 and -15%) in monthly SO4—bulk and throughfall deposition
from 1986 to 1997 that was significant for 11 out of 13 plots in Norway. Moffat et al. (2002)
noted that the small but significant decrease of almost all ions in throughfall deposition from
1987 to 1997 of these plots was in line with earlier findings of Likens et al. (1996) and
Stoddard et al. (1999) for stream water chemistry in North America and Europe.
When Rogora et al. (2006) compiled an overview of trend of bulk and throughfall deposition
in the Alps for the two periods 1985-2002 and 1990-2002 they found that the decreasing
trends of N deposition were still significant at the minority of sites (about 25% of the sites for
NO3- and 50% of the sites for NH4+) while SO4— trends were clearly significant at all sites
with the Seasonal Mann-Kendall that is more conservative than linear regression used for the
maps here. Hence, for the 2001 to 2010 period, decreasing trends seem to flatten for SO 4— as
well as for NH4+ and NO3-.
Moreover, Vanguelova et al. (2010) found SO4— decrease that were significant at only 4 out
of 10 plots for bulk deposition but at 8 out of 10 plots for throughfall deposition in the
monthly 1995 to 2006 deposition data in UK. In bulk deposition the background level of sea
salt sulphur deposition might be relatively high at ? some of the costal plots in UK. Same
magnitudes of absolute decreasing trends might thus result in lower relative slopes of the
trends at these sites, especially for bulk deposition.
Fagerli et al. (2008) compared NO3- and NH4+ concentration in wet precipitation modelled by
EMEP based on the emissions inventories with measurements for the period from 1980 or
later to 2003 and various sites in Europe. They also found decreasing trends being significant
for about half of the sites for both modelled and measured data. The significant decreases
ranged from about 20% to 60% in 20 years, corresponding thus to relative slope of about 1%
to 6% a-1. However, most of the reductions seem to have taken place in the years between
1985 and 1995. Other reasons for decreasing deposition are changes in the tree stand structure, such as the reduction of the number or trees due to a bark beetle attack that occurred on a
Czech plot (2161).
However, atmospheric deposition values presented here are restricted to bulk and throughfall
deposition fluxes of inorganic compounds. Total deposition to forests also includes organic
compounds, stemflow, as wells as canopy uptake. Especially for nitrogen, total deposition
typically is significantly higher than the throughfall fluxes.
73
4.6. Conclusions
In about half of the sites a decrease of N and S was observed in the periods 2001 to 2010 and
2005 to 2010 that was strong enough to be identified with statistical significance.
It could be confirmed that the selection of the trend analyses techniques has an effect on trend
detection. There was a quite high agreement in estimated trend slopes. However, Seasonal and
Partial Mann-Kendall tests applied to monthly data tend to detect smaller trends with statistical significance than linear regression techniques applied to annual data.
There was a quite consistent relation between the relative slope and p-value of the trend tests
for a given length of time series independent of the trend analyses technique used. These patterns also varied surprisingly little between ions. It seems likely that the minimal detectable
trend depends mainly on the length of the time series. For time series with a length of 10
years, the minimal detectable trends for N and S compounds seem to be around 3% change
per year for linear regression techniques applied to annual data, and around 1% to 2% for Partial Mann-Kendall tests applied to monthly data.
For N compounds, the trends in atmospheric deposition expected to result from the emission
reductions in Europe typically are in this range and are unlikely to be detected with statistical
significance in time series with a length of much less than 10 years of measurement. For S
compounds, typical trends were often higher especially in the 90ties favouring a trend detection with statistical significance.
The deposition trends found in this evaluation are thus quite comparable to those estimated by
the European Monitoring and Evaluation Programme from emission inventories. However, its
worth noting that the bulk and throughfall fluxes presented here cannot directly be compared
to the total atmospheric deposition as estimated by EMEP.
4.7. Acknowledgements
The method for determination of atmospheric deposition fluxes applied in ICP-Forests has
been further developed and harmonized by numerous scientists within in the Expert Panel on
Deposition that has subsequently been chaired by Erwin Ulrich, Nicolas Clarke and Karin
Hansen during the years this investigation is focused on. Application of the methods involved
numerous field technicians to install about 4000 samplers, local forest services and field observers to collect more than about a million individual samples, chemical analyses of about
200’000 pooled samples and on-going supervision by about 40 responsible scientists. Data
transmission involved national focal centres and the data centre of ICP Forests that was subsequently at FIMCI in the Netherlands, at the Joint Research Centre in Ispra and is today at
the Programme Coordination Centre in Hamburg. Data transmission included sophisticated
conformity and plausibility checks developed by the involved data base specialists. The comparability of the laboratory analyses has been improved by the activities of the Working
Group on QA/QC in labs (standardisation of lab methods, exchange of experience and working ring-tests) initiated and supported by Rosario Mosello, Nils König, Erwin Ulrich, Kirsti
Derome, Anna Kowalska, Aldo Marchetto and others. Similarly activities for the field installations were organised by Albert Bleeker, Jan Erisman, Erwin Ulrich, Daniel Zlindra and others. Data quality objectives used here and other methodical improvement were the result by
the activities of the QA/QC committee chaired by Marco Ferretti. The atmospheric deposition
measurement was established in the frame of the ICP-Forests Programme and was enabled
through various ways. Typically this involved access granted by public or private land owners, financial support from the participating countries and the EU, as well as support from
74
subordinated governmental organisations such as districts, communities or even local forest
services.
4.8. References
Bleeker, A., Draaijers, G., van der Veen, D., Erisman, J. W., Mols, H., Fonteijn, P. & Geusebroek, M., 2003: Field intercomparison of throughfall measurements performed within
the framework of the Pan European intensive monitoring program of EU/ICP Forest.
Environmental Pollution, 125 (2): 123-138.
Boháčová, L., Lomský, B., Šrámek, V., Fabianek, P., Fadrhonsova, V., Lachmanova, Z.,
Novotny, R., Uhlifova, H. & Vortelova, L., 2010: Forest Condition Monitoring in the
Czech Republic. Annual Report of ICP Forests/FutMon Programme data 2008 and
2009. Forestry and Game Management Research Institute of the Czech Republic
(FGMRI), Jiloviště-Strnady, Czech Republic, 157 p.
Erisman, J. W., Mols, H., Fonteijn, P., Geusebroek, M., Draaijers, G., Bleeker, A. & van der
Veen, D., 2003: Field intercomparison of precipitation measurements performed
within the framework of the Pan European Intensive Monitoring Program of EU/ICP
Forest. Environmental Pollution, 125 (2): 139-155.
Fagerli, H. & Aas, W., 2008: Trends of nitrogen in air and precipitation: Model results and
observations at EMEP sites in Europe, 1980-2003. Environmental Pollution, 154: 448461.
Graf Pannatier, E., Thimonier, A., Schmitt, M., Walthert, L. & Waldner, P., 2011: A decade
of monitoring at Swiss Long-term Forest Ecosystem Research (LWF) sites: can we
observe trends in atmospheric acid deposition and in soil solution acidity? Environmental Monitoring and Assessment, 174: 3-30.
Granke, O. & Mues, V., 2010: Sulfur and nitrogen deposition and its trends. In: R. Fischer,
M. Lorenz, O. Granke, V. Mues, S. Iost, H. Van Dobben, G. J. Reinds & W. De Vries
(eds.), The Condition of Forests in Europe. 2010 Technical Report of ICP Forests.
UN-ECE Convention on Long-Range Air Pollution, International Co-opearative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICPForests) and European Commission, Hamburg, p. 45-53.
Helsel, D. R. & Hirsch, R. M., 2002: Statistical methods in water resources. Water-Resources
Investigations, Vol. A3, 524 p.
Hirsch, R. M. & Slack, J. R., 1984: A nonparametric trend test for seasonal data with serial
dependence. Water Resources Research, 20: 727-732.
Hirsch, R. M., Slack, J. R. & Smith, R. A., 1982: Techniques of trend analysis for monthly
water-quality data. Water Resources Research, 18 (1): 107-121.
Houston, T. J., Durrant, D. W. & Benham, S. E., 2002: Sampling in a variable environment:
selection of representative positions of throughfall collectors for volume and chemistry under three tree species in the UK. Forest Ecology and Management, 158 (1-3): 18.
ICP Forests, 2010: Manual on methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests. International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP-Forests). Convention on Long-Range Transboundary Air Pollution
(LRTAP). UN-ECE., Hamburg, Prague, numerous p.
Kvaalen, H., Solberg, S., Clarke, N., Torp, T. & Aamlid, D., 2002: Time series study of concentrations of SO42- and H+ in precipitation and soil waters in Norway. Environmental Pollution, 117 (2): 215-224.
75
Lange, H., Solberg, S. & Clarke, N., 2006: Aluminium dynamics in forest soil waters in Norway. Science of the Total Environment, 367: 942-957.
Libiseller, C. & Grimvall, A., 2002: Performance of partial Mann-Kendall tests for trend detection in presence of covariates. Environmetrics, 13: 71-84.
Likens, G. E., Driscoll, C. T. & Buso, D. C., 1996: Long-term effects of acid rain: response
and recovery of a forest ecosystem. In: Science, Vol. 272, pp. 244-246.
Lorenz, M. & Granke, O., 2009: Deposition measurements and critical loads calculations:
monitoring data, results and perspective. iForest - Biogeosciences and Forestry, 2 (1):
11-14.
Mann, H. B., 1945: Non-parametric tests against trends. Econometrica, 13: 245-259.
Marchetto, A., 2012: RKT. R function, ISE-CNR, Pallanza, Italy,p.
Marchetto, A., Mosello, R., Tartari, G., Derome, J., Derome, K., König, N., Clarke, N. &
Kowalska, A., 2011: Atmospheric Deposition and Soil Solution Working Ring Test
2009. Report Institute for Ecosystem Studies Vol. CNR-ISE 04.09, EU/Life+ Project
FutMon, Verbania Pallanza, 41 p.
Marchetto, A., Mosello, R., Tartari, G., Derome, J., Derome, K., Sorsa, P., König, N., Clarke,
N., Ulrich, E. & Kowalska, A., 2006: Atmospheric deposition and soil solution
Working Ring Test 2005 - Laboratory ring test for deposition and soil solution sample
analyses between the countries participating in the ICP Forests Level II monitoring
programme. Office National des Forêts, Département Recherche, 85 p.
Meesenburg, H., Meiwes, K. J. & Rademacher, P., 1995: Long term trends in atmospheric
deposition and seepage output in northwest German forest ecosystems. In: Water, Air,
and Soil Pollution, Vol. 85, pp. 611-616.
Moffat, A. J., Kvaalen, H., Solberg, S. & Clarke, N., 2002: Temporal trends in throughfall
and soil water chemistry at three Norwegian forests, 1986-1997. Forest Ecology and
Management, 168 (1-3): 15-28.
Mosello, R., Brizzio, M. C., Kotzias, D., Marchetto, A., Rembges, D. & Tartari, G., 2002:
The chemistry of atmospheric deposition in Italy in the framework of the National
Programme for Forest Ecosystems Control (CONECOFOR). Journal of Limnology, 61
(Suppl. I): 77-92.
Pihl Karlsson, G., Akselsson, C., Hellsten, S. & Karlsson, P. E., 2011: Reduced European
emissions of S and N – Effects on air concentrations, deposition and soil water chemistry in Swedish forests. Environmental Pollution, 159 (12): 3571-3582.
Rogora, M., Mosello, R., Arisci, S., Brizzio, M., Barbieri, A., Balestrini, R., Waldner, P.,
Schmitt, M., Stähli, M., Thimonier, A., Kalina, M., Puxbaum, H., Nickus, U., Ulrich,
E. & Probst, A., 2006: An overview of atmospheric deposition chemistry over the
Alps: Present status and long-term trends. Hydrobiologia, 562: 17-40.
Rogora, M., Mosello, R. & Marchetto, A., 2004: Long-term trends in the chemistry of atmospheric deposition in Northwestern Italy: the role of increasing Saharan dust deposition.
Tellus Series B, 56 (5): 426-434.
Kendalls Tau. Journal of the American Statistical Association, 63 (324): Sen, P. K., 1968:
Estimates of regression coefficient based on 1379-&.
Staelens, J., De Schrijver, A., Verheyen, K. & Verhoest, N. E. C., 2006: Spatial variability
and temporal stability of throughfall deposition under beech (Fagus sylvatica L.) in relationship to canopy structure. Environmental Pollution, 142 (2): 254-263.
Stoddard, J. L., Jeffries, D. S., Lukewille, A., Clair, T. A., Dillon, P. J., Driscoll, C. T., Forsius, M., Johannessen, M., Kahl, J. S., Kellogg, J. H., Kemp, A., Mannio, J., Monteith,
D. T., Murdoch, P. S., Patrick, S., Rebsdorf, A., Skjelkvale, B. L., Stainton, M. P.,
Traaen, T., van Dam, H., Webster, K. E., Wieting, J. & Wilander, A., 1999: Regional
76
trends in aquatic recovery from acidification in North America and Europe. In: Nature,
Vol. 401, pp. 575-578.
Thimonier, A., 1998: Measurement of atmospheric deposition under forest canopies: some
recommendations for equipment and sampling design. Environmental Monitoring and
Assessment, 53 (3): 353-387.
Thimonier, A., Schmitt, M., Waldner, P. & Rihm, B., 2005: Atmospheric deposition on Swiss
Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and
Assessment, 104: 81-118.
Vanguelova, E. I., Benham, S., Pitman, R., Moffat, A. J., Broadmeadow, M., Nisbet, T., Durrant, D., Barsoum, N., Wilkinson, M., Bochereau, F., Hutchings, T., Broadmeadow,
S., Crow, P., Taylor, P. & Durrant Houston, T., 2010: Chemical fluxes in time through
forest ecosystems in the UK - Soil response to pollution recovery. Environmental Pollution, 158 (5): 1857-1869.
Wu, Y., Clarke, N. & Mulder, J., 2010a: Dissolved organic carbon concentrations in throughfall and soil waters at Level II monitoring plots in Norway: short- and long-term variations. Water, Air, & Soil Pollution, 205 (1): 273-288.
Wu, Y., Clarke, N. & Mulder, J., 2010b: Dissolved organic nitrogen concentrations and ratios
of dissolved organic carbon to dissolved organic nitrogen in throughfall and soil waters in Norway spruce and Scots pine forest stands throughout Norway. Water, Air, &
Soil Pollution, 210 (1): 171-186.
Zlindra, D., Eler, K., Hansen, K. & Clarke, N., 2011: Report on the experimental installation
of standardized throughfall collectors. ed. E. L. P. p. F. report, Slovenien Forest Research Institute, 122 p.
77
5. Exceedance of critical limits and their impact on tree nutrition
P. Waldner, A. Thimonier, E. Graf Pannatier, A. Marchetto, M. Ferretti, M. Calderisi, P. Rautio, K. Derome, T.
Nieminen, S. Nevalainen, A.-J. Lindroos, P. Merilä, G. Kindermann, M. Dobbertin, M. Neumann, N. Cools, B.
de Vos, P. Roskams, K. Hansen, H.-P. Dietrich, R. Fischer, O. Granke, S. Iost, M. Lorenz, S. Meining, H.-D.
Nagel, P. Simoncic, K. v. Wilpert, H. Meesenburg, A. Verstraeten, S. Nevalainen, T. Scheuschner, M. Ingerslev,
S. Raspe1.
The atmospheric deposition of sulphur (S) and nitrogen (N) compounds affects forest ecosystems through several processes. For N limited stands, enhanced N supply may stimulate the
production of above-ground biomass. However, in excess, N loads may lead to nutrient imbalances and sensitivity to frost, insects, and fungi may increase (N saturation hypothesis,
postulated by Aber et al., 1989). When N availability exceeds the capacity of the ecosystem to
retain N, nitrate leaching from the rooting zone is enhanced. The release of acid anions such
as nitrate (NO3-) and sulphate (SO4--), balanced by base cation leaching, may contribute to the
acidification of soils and surface waters (acidification hypothesis, postulated by Aber et al.,
1989). In acid soils, aluminium (Al) can be mobilized from the soil complex and have adverse
effects on fine roots. Both the loss of base cations and the toxic effect of aluminium may further contribute to an unbalanced mineral nutrition of the trees.
The long-term effects of atmospheric deposition on ecosystems can be assessed using the
concept of “critical loads” and “critical levels” (CL). These critical values are defined as a
quantitative estimate of an exposure to loads or levels below which significant harmful effects
on specified sensitive elements of the environment do not occur according to current knowledge (Nilsson & Grennfelt, 1988).
The BC/Al molar ratio, where BC corresponds to the sum of the base cations Ca2+, Mg2+ and
K+, is the most widely used criterion for estimating CL for acidity (ALBIOS project, Cronan
et al., 1989; ICP-Modelling and Mapping, Spranger et al., 2004; ICP-Forests Technical Report 2010, Iost, 2010).
For nitrogen, CL have been defined with three approaches: the first approach gives a range of
typical critical loads for each ecosystem type, e.g. for forests 10 to 20 kg ha-1 a-1 (empirical
CL, Achermann & Bobbink, 2003). The second approach assumes that the leaching of N below rooting zone should not exceed an ‘acceptable’ level. The third approach uses values of N
concentrations in the soil solution as a criterion for e.g. nutrient imbalances, N saturation or
enhanced sensitivity to frost and fungal diseases (Sverdrup & de Vries, 1994; Lorenz et al.,
2008; Technical Report 2011, Iost et al., 2011).
Commonly, a steady state approach (SMB) is used for the European-wide mapping of critical
loads exceedances: It is assumed that the ecosystem will reach a steady state and the maximal
acceptable load that ensures that a certain criterion remains within defined limits is estimated
for this state (cf. Mapping Manual, Spranger et al., 2004; Level I and II plots Nagel et al.,
2011). The base cations to aluminium ratio (BC/Al) in the soil solution and the ‘acceptable’ N
leaching are among the criteria typically used to define CL for acidity and nutrition N.
On ICP Forests plots, ecosystem responses to deposition are monitored and this data seems to
be suited to investigate tree response to critical loads exceedances. However, plots marked on
the maps as having CL exceeded may not yet have reached the steady state and changes of
soil solution and related effects are thus expected to appear in future only.
1
For addresses see Annex III-4
78
In this context, ICP Forests allow to investigate the actual N saturation and acidification status
of plots with CL exceeded as well as the tree response to exceedances of critical limits in soil
solution.
5.1. Objectives
This study aims at investigating the following relations based on the Level II network:

Relations between exceedance of critical loads and indicators for N saturation status

Relations between exceedance of critical loads and indicators for soil acidification
status

Relations between exceedance of critical limits for soil solution and tree responses.
5.2. Methods
This study has been carried out on the plots from the countries shown in Table 5.2-1 of the
Level II network of ICP-Forests programme (Fischer et al., 2010), i.e. the intensive monitoring plots of the FutMon project of the LIFE+ programme based on measurements and assessment carried out during 2006-2009.
Table 5.2-1: Study sites and number of plots for combinations of critical loads calculations (CL SMB), the deposition (DP), soil solution (SS) and foliar (FO) analyses and assessment of light green to yellow discolouration or
Mg deficiencies (damage cause assessment, DCA) in the years 2007 to 2009.
COUNTRY
CLSMB>SS DP>SS
SS>FO
DP>FO
yellowing
plots
plots plots species plots plots species
France
9
14
14
1
24
Belgium
4
7
7
1
9
11
1
Netherlands
1
1
1
4
Germany
5
59
53
1.2
62
31
1
Italy
7
9
9
1.1
22
20
1.8
United Kingdom
1
6
5
1.2
5
5
1
Ireland
2
2
Greece
3
3
3
1
4
3
1
Spain
3
3
1
14
34
1.1
Sweden
5
1
Austria
1.1
20
Finland
15
17
17
1.0
18
15
1.1
Switzerland
7
7
1.6
12
Hungary
1
1
1
8
5
1.8
Romania
3
Poland
1
1
1.0
1
18
1
Slovak Republic
3
3
4
1.2
6
6
3.8
Norway
1
8
6
1.2
Lithuania
1
3
4
1.3
Czech Republic
8
11
11
1
12
14
1.1
Estonia
3
3
1
7
5
1
Slovenia
1
6
7
1
Russian Federation
3
Bulgaria
1
3
Latvia
1
1
1
1
Cyprus
1
2
2
1.5
n
Deficiencies
n
111
18
281
78
22
122
10
805
19
58
12
32
205
29
4
73
30
15
51
CLSMB>SS: plots for which CLSMB and SS were determined, etc. yellowing: reporting of occurrence of symptom ‘light yellow to green discolouration’, deficiencies: reporting of occurrence of cause ‘nutrient deficiencies’. plots: number of plots, species: mean number of tree
species per plot, n: number of trees.
79
The measurements and assessments have been carried out according to the Manual of ICPForests (ICP-Forests, 2010). For detailed additional descriptions refer to e.g. Thimonier et al.
(2010) [list of national studies, to be extended]. The QA/QC measures included checks within
the countries, as well as consistency checks during the submission of the data to the data centre at the Programme Coordination Centre (PCC).
Bulk deposition (BD) and throughfall deposition (TF) were continuously collected at weekly
to monthly sampling intervals. Annual deposition was calculated as described in the ICP Forests Technical Report (Granke & Mues, 2010), using continuous sampling during at least 333
days as completeness criterion. Volume of bulk deposition was used to derive precipitation
quantity.
Soil solution was sampled generally with suction cup lysimeters at same intervals as deposition and analysed chemically in laboratory. Annual mean concentrations of the samples as
well as the proportion of samples exceeding critical limits were calculated for each depth and
depth classes were aggregated as described in the Technical Reports (Iost, 2010; Iost et al.,
2011) (Table 5.2-2). For the comparison of critical loads exceedance and soil solution a generic critical BC/Al=0.8 was used, while for comparison of soil solution and foliar nutrition
the tree species specific values in Table 5.2-2 were applied. The values of the lowest lysimeter
are referred to as ‘bottom’ hereafter.
Table 5.2-2: Critical limits for N in soil solution (Mapping Manual, Spranger et al., 2004; ICP-Forests Technical
Report 2010, Iost, 2010) and indicators of the damage cause assessment (DCA) and the foliar analyses (FO).
Effect
forest type
Indicators compared
Nutrient imbalances
critical limit
N (mg N/L)
0.2
0.4
depth
class used
Coniferous mineral
Deciduous topsoil
N saturation
1
All
Foliar N, Mg and K nutrition (FO), symptoms of ‘light green to yellow discolouration’ and cause ‘Mg deficiency’ (DCA).
(exceedance of critical loads)
enhanced sensitivity to frost
and fungal
disease
3
All
lowest
lysimeter
mineral
topsoil
Cause ‘fungal disease’ on foliage (DCA)
Foliage samples were taken from the upper third of the tree canopy from branches fully exposed to sunlight. Foliage was sampled from at least five trees of the main tree species on the
plot. In case of deciduous species, foliage was sampled during full development of the leaves,
i.e. in the end of growing season before autumn yellowing. Evergreen foliage was sampled
during dormancy period. The ranges of optimal nutrition and the species group compiled by
the Expert Panel of Foliage FSCC (Stefan et al., 1997) were used to classify the nutrient concentrations in foliage into ‘low’, ‘in’ and ‘high’ (Table 5.2-3).
80
Table 5.2-1: Ranges of optimal nutrition for tree foliar concentrations (mg/kg) of nitrogen (N), phosphorus (P),
potassium (K), calcium (Ca) and magnesium (Mg) compiled by FSCC (Stefan et al., 1997) and critical values of
the molar ratio of base cations to total aluminium (BC/Al crit) in the soil solution according to Sverdrup et al.
(1994) and Lorenz et al. (2008).
Species group
Spruce
Silver Fir
Pine
Douglas
other conifers
Beech
Birch
Oak
other broadleaves
N
12 - 17
12 - 17
12 - 17
12 - 17
12 - 17
18 - 25
18 - 25
15 - 25
15 - 25
P
1-2
1-2
1-2
1-2
1-2
1 - 1.7
1 - 1.7
1 - 1.8
1 - 1.8
K
3.5 - 9
3.5 - 9
3.5 - 10
3.5 - 10
3.5 - 10
5 - 10
5 - 10
5 - 10
5 - 10
Ca
1.5 - 6
1.5 - 6
1.5 - 4
1.5 - 4
1.5 - 4
4-8
4-8
3-8
3-8
Mg
0.6 - 1.5
0.6 - 1.5
0.6 - 1.5
0.6 - 1.5
0.6 - 1.5
1 - 1.5
1 - 1.5
1 - 2.5
1 - 2.5
BC/Alcrit
1.2
1.2
1.2
0.3
1.2
0.6
0.8
0.6
0.6
The Damage Cause Assessment can be carried out and reported in various level of detail, e.g.
only symptom and cause class or Latin name of causing insect. Assessment of a specific
symptom detail has been assumed to be carried out if it has been reported at least once in a
country and year. Based on this assumption, the proportion of trees showing the symptom has
been computed for each year.
Critical loads for nitrogen as a nutrient Nnut (CLNSMB) and for acidification (CLASMB) have
been calculated as described in Nagel et al. (2011), based on the measurements described
above as well as on soil analyses, using the methods recommended in the Mapping Manual
(Spranger et al., 2004). On plots without steady-state mass balance (SMB) calculations, empirical critical loads for Nnut (typically in the range between 10 to 20 kg N ha-1 a-1, Achermann and Bobbink, 2003) were used as a reference to classify N loads. Throughfall N deposition was used for calculation of critical loads exceedances.
Means of annual values of the period 2006 - 2009 have been calculated per plot and tree species. The means of the exceedances of critical loads and critical limits and the means of foliar
concentrations and occurrence of symptoms per tree species were compared hereafter.
Relations were investigated comparing the percentage of plots using contingence tables and
the chi-square test. In addition linear mixed effects models (LME, Pinheiro and Bates, 2011)
were applied on annual means from 2006 to 2009. Foliar N and Mg concentrations were used
as response variables and the concentrations in soil solution of N and Mg, as well as precipitation, altitude and latitude as explanatory variables. Fixed effects of species group and random
effect of survey year nested per plot were included into the regression modelling using the
maximum likelihood method.
5.3 Results
Figure 5.3-1 illustrates the changes of the concentrations of dissolved inorganic nitrogen
(NH4++ NO3-) in the water on its path through the ecosystem, as already shown earlier by e.g.
de Vries et al. (2003). Despite direct uptake of N from the canopy, the precipitation below
canopy (throughfall) often has higher N concentrations than above (values typically close to
the bulk deposition), due to dry deposition on the foliage that is washed out with the precipitation. Total deposition of N to forests is thus normally higher than to open land and even
higher than the throughfall N deposition shown here. Subsequent concentrations in soil solution are the net result of various processes including immobilisation of nitrogen in the solid
phase, nutrient uptake by vegetation, nutrient release from decomposed biomass and water
volume changes through e.g. transpiration.
15
10
0
5
soil solution organic layer(mg N/L)
15
10
5
0
throughfall (mg N/L)
20
81
20
0
5
10
15
0
5
10
15
15
20
15
10
5
0
soil solution mineral subsoil (mg N/L)
20
15
10
5
0
10
0
soil solution mineral topsoil (mg N/L)
bulk deposition (mg N/L)
5
0
5
10
15
Figure 5.3-1: Concentration of dissolved inorganic nitrogen (NH4+ + NO3-) in bulk precipitation, throughfall
precipitation, and soil solution (top right: organic layer, bottom left: mineral topsoil 0-40 cm and bottom right:
mineral subsoil 40-80 cm) on selected ICP-Forests level II plots.
The capacity of ecosystems to accumulate nitrogen depends on the N saturation state, which is
affected by various processes (Gundersen et al., 1998; MacDonald et al., 2002; Gundersen et
al., 2006; Gundersen et al., 2009). Figure 5.3-2 shows that on plots with higher N throughfall
(>20 kg N ha-1 a-1), nitrate concentrations in the soil solution samples from the lowest lysimeters more often exceeded critical limits for N saturation. Regarding the SMB concept, we may
interpret that about half of the plots with critical loads exceeded already show indication of N
saturation while the other half may still be in the phase of accumulation and reach saturation
later (Figure 5.3-2). We investigated the relation between exceedance of critical loads for eu-
82
trophication and exceedance of criticical limits for nitrate in soil solution by using throughfall
as indicator for exceedance of empirical critical loads. This way a higher number of plots
could be included in the analyses, because SMB critical loads have only been calculated for
much less plots.
70%
60%
50%
40%
30%
20%
10%
0%
0 - 10 (n=83)
10 - 20 (n=80)
20 - 30 (n=15)
-1
30 - 40 (n=5)
-1
N-deposition (kg N ha a in throughfall)
Proportion of samples showing exceedance
of critical limits for N saturation in soil solution (NO3 > 1 mg N/L)
0%
0%-50%
50%-100%
Figure 5.3-2: Percentage of plots with critical limits for N saturation (NO3- > 1 mg/L) in soil solution of the
lowest lysimeter exceeded in no sampling period (0%), less than half of the samples (0-50%) and more than half
of the samples (50-100%). These percentages plots have calculated for four categories of plots that were defined
according to the amount of throughfall N deposition (less than 10, between 10 and 20 and exceeding 20 kg N ha1 a-1).
83
Table 5.3-1: Percentage of plots with exceedances of critical limits for BC/Al in soil solution in more than 80%
of the soil solution samples for plots with and without exceedances of critical loads for Acidity CLASMB. The
critical BC/Al ratio of 0.8 was used for all plots regardless of the tree species composition on the plot.
depth class
CLASMB
0-20cm
not exc.
exceeded
not exc.
exceeded
not exc.
exceeded
not exc.
exceeded
20-40cm
40-80cm
below 80
cm
BC/Al < 0.8
<80% >80%
65%
35%
82%
18%
74%
26%
50%
50%
46%
54%
38%
63%
64%
36%
50%
50%
n
(57)
(11)
(35)
(6)
(28)
(8)
(14)
(2)
Similarly, the proportion of plots with BC/Al criterion exceeded (BC/Al<0.8 in more than
80% of the samples) seems to be higher among the plot with exceedance of critical loads of
acidity than among the plots without exceedance of critical loads for acidity (Table 5.3-1).
Laboratory determination of the Al speciation showed that the most toxic form, Al3+, typically
is about 30% to 100% of total dissolved aluminium, but can also be much lower (Graf Pannatier et al., 2011).
Figure 5.3-3:
Throughfall N deposition measurements (left) and exceedances of critical limits of N in soil
solution (percentage of samples from lowest lysimeter with sum of concentrations of NO 3- +NH4+ >1 mg N/L)
for N saturation (right) on ICP-Forests level II plots. Mean of annual deposition with at least 330 day of continuous measurement for the period 2006 to 2009.
The relation between the exceedance of critical limits in soil solution for nutrient imbalances
and the class of nutritional status as indicated by the foliar concentrations are shown in Table
5.3-2 and Table 5.3-3 (see also Figure 5.3-3, Figure 5.3-5). Tree species had been summarized
to tree species groups in Table 5.3-3.
84
Generally, coniferous species more often have N values falling into the nutrition class ‘low’
than the broadleaves. This may be related to the fact that conifers are naturally abundant in
higher altitudes and latitudes, both regions with lower N deposition (Thimonier et al., 2010).
When comparing plots with exceedance to plots without exceedance of critical limits for NO3in soil solution, there seems to be a shift to higher classes for N for Spruce, Pines and Oak and
a shift to lower classes for Mg for Spruce, Pines, and Beech. For the groups of Spruce and
Pines, the percentage of plots with foliar N concentrations below optimal range is significantly lower for plots with critical limits exceeded. For conifers, Mg concentrations below
optimal ranges were only recorded on plots with critical limits exceeded. For Beech, the percentage of plots with low Mg values is higher for plots with critical limits exceeded. For these
latter observations, statistical significance was not reached with the chi-square test. However,
the LME analyses resulted in a significant positive effect of the N concentration in soil solution on the N concentrations in foliage (p<0.000) and negative effect on Mg concentrations
(p=0.01) and Mg/N ratio (p<0.000). For Spruce, Pines, Beech and Oak there is a tendency
towards less optimal Mg/N ratios with increasing exceedances of critical limits for N in soil
solution.
Table 5.3-2: Relation between exceedance of critical limits for NO3- in soil solution sampled in the mineral
topsoil (0-40 cm depth) (TR, Iost, 2010) and foliar nutrition classes (FSCC, Stefan et al., 1997) assigned to foliage samples from 2007 to 2009. Percentages of plots with mean foliage concentrations of N, Mg, and K below,
in or above the range for optimal nutrition for plots with soil solution critical limits for nutrient imbalances exceeded in 0% and more percent of soil solution samples from the mineral topsoil, respectively.
Species group NO3-N
N
(mg N/l)
low
in
Spruce
0.2 not exc. 38% 62%
exceeded 7% 93%
Pines
0.2 not exc. 22% 67%
exceeded 0% 79%
Fir
0.2 not exc.
0% 100%
exceeded 20% 80%
Beech
0.4 not exc.
0% 71%
exceeded 0% 72%
Oak
0.4 not exc.
0% 83%
exceeded 0% 35%
Bold: p-value<0.05 with chi-square test.
High
0%
0%
11%
21%
0%
0%
29%
28%
17%
65%
n
(13)
(42)
(9)
(34)
(1)
(10)
(7)
(39)
(6)
(17)
Mg
low
0%
2%
0%
6%
0%
10%
10%
36%
0%
0%
in
79%
93%
89%
94%
0%
60%
30%
33%
100%
100%
high
21%
5%
11%
0%
100%
30%
60%
31%
0%
0%
n
(14)
(42)
(9)
(34)
(1)
(10)
(10)
(39)
(6)
(17)
K
low
7%
7%
0%
0%
0%
0%
0%
3%
0%
0%
in
93%
93%
100%
100%
100%
100%
100%
85%
83%
76%
high
0%
0%
0%
0%
0%
0%
0%
13%
17%
24%
n
(14)
(42)
(9)
(34)
(1)
(10)
(10)
(39)
(6)
(17)
For the relation between ‘exceedance’ of critical BC/Al in soil solution and foliar nutrition, it
is more difficult to interpret the results shown in Table 5.3-3. However, for the species group
Fir, Mg concentrations in foliage below optimum were more frequent on plots with BC/Al <
1.2.
85
Table 5.3-3: Relation between exceedance of critical BC/Al ratio in soil solution sampled in the mineral topsoil
(0-40 cm depth) (TR, Iost et al., 2011) and foliar nutrition classes (FSCC, Stefan et al., 1997). Percentages of
plots with mean foliage concentrations of N, Mg, and K below, in or above range for optimal nutrition for plots
with BC/Alcrit exceeded in less and more than 80% of soil solution samples, respectively.
Tree species
Spruce
crit.
BC/Al
1.2
Pines
1.2
Fir
1.2
Douglas Fir
0.3
Other conifers
Beech
1.2
Oak
0.6
Birch
0.8
0.6
Other broadl. 0.8
exceed.
(sampl.)
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
<80%
>80%
N
low
20%
5%
0%
8%
14%
25%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
in
80%
95%
84%
71%
86%
75%
0%
100%
0%
0%
76%
58%
47%
50%
100%
0%
50%
0%
high
0%
0%
16%
21%
0%
0%
0%
0%
0%
0%
24%
42%
53%
50%
0%
0%
50%
0%
n
(35)
(20)
(19)
(24)
(7)
(4)
(0)
(1)
(0)
(0)
(34)
(12)
(19)
(4)
(2)
(0)
(2)
(0)
Mg
low
0%
5%
5%
4%
0%
25%
0%
0%
0%
0%
36%
15%
0%
0%
0%
0%
0%
0%
in
86%
95%
95%
92%
57%
50%
0%
0%
0%
0%
31%
38%
100%
100%
0%
0%
75%
0%
high
14%
0%
0%
4%
43%
25%
0%
100%
0%
0%
33%
46%
0%
0%
100%
0%
25%
0%
n
(36)
(20)
(19)
(24)
(7)
(4)
(0)
(1)
(0)
(0)
(36)
(13)
(19)
(4)
(2)
(0)
(4)
(0)
K
low
3%
15%
0%
0%
0%
0%
0%
0%
0%
0%
3%
0%
0%
0%
0%
0%
0%
0%
in
97%
85%
100%
100%
100%
100%
0%
100%
0%
0%
89%
85%
79%
75%
100%
0%
75%
0%
high
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
8%
15%
21%
25%
0%
0%
25%
0%
n
(36)
(20)
(19)
(24)
(7)
(4)
(0)
(1)
(0)
(0)
(36)
(13)
(19)
(4)
(2)
(0)
(4)
(0)
BC/Alcrit: growth reduction to 80% of mean according to (Sverdrup & de Vries, 1994; Lorenz et al., 2008) (no data for Sitka Spruce, few
data only), n: number of plots.
The symptom of ‘light green to yellow discolouration’ and the cause ‘nutrient deficiency’
have not been assessed on all plots. The assessment does not completely cover the full range
of foliar concentrations found in the whole dataset. Symptoms reported from a plot might
have appeared on trees not sampled for foliage analyses. However, foliar Mg concentrations
of sampled trees on plots from which symptoms were reported are in the lower part of the
concentration range of the tree species (not shown).
Figure 5.3-4 shows that reported ratio of trees with the symptom is higher on plots with soil
solution critical limits for nutrient imbalances exceeded. In Finland, where the N nutrition was
generally rather low, deformations of needles and branches (wilting, curving, witch’s brooms,
etc.) have been observed on Scots pines and Norway spruce.
86
80
60
40
0
0
0.5
1
0
0.5
1
n=20
n=63
n=24
n=26
n=67
n=50
Beech
Silver Fir
80
60
40
20
0
20
40
60
ratio of trees (%)
80
100
exc. crit. N in soil solution
100
0
ratio of trees (%)
20
ratio of trees (%)
80
60
40
20
0
ratio of trees (%)
100
Spruce
100
Pines
0
0.5
1
0
0.5
1
n=9
n=25
n=21
n=2
n=8
n=9
Figure 5.3-4: Percentage of trees with symptom ‘light green to yellow discolouration’ for plots with critical
limits for N in soil solution for nutrient imbalances exceeded in 0%, between 0% and 50%, and >50% of the
samples from the mineral topsoil. The relation is shown for the species groups ‘Spruce’ (top left), ‘Pines’ (top
right), ‘Beech’ (bottom left), and ‘Silver Fir’ (bottom right) (n: number of plots).
87
Figure 5.3-5: Foliar nutrition class (FSCC, Stefan et al., 1997) for N (top left), Mg (top right) and K (bottom
left) concentrations of the main tree species, and occurrence of the symptom ‘light green to yellow discoloration’
of foliage reported within the Damage Cause Assessment (bottom right). Means of available values of the years
2006 to 2009 for ICP-Forests level II plots are shown.
88
Similar relations have been found between exceedance of BC/Al ratio and ratio of trees with
reporting of cause ‘insects’, but not for cause ‘fungal disease’ on foliage, with the current set
of assessment based on the assumptions mentioned in the method sections.
40
30
20
10
0
ratio of trees(%)
50
All species
0
0.5
1
n=38
n=150
n=75
Figure 5.3-6: Percentage of trees with symptom cause ‘nutrient deficiency’ reported for plots with critical limits
for N in soil solution for nutrient imbalances exceeded in 0% (“0”), between 0% and 50% (“0.5”), and >50%
(“1”) of the samples from the mineral topsoil (n: number of plots).
5.4 Discussion
A correlation or a relation between two variables does not necessary mean that there is a
cause effect relationship. There might be confounding factors. In this study the influence of
e.g. soil condition, tree age, tree density, management, and drought stress has not been considered.
Critical loads are a concept to prevent long-term effects. Absence of immediate effects of
critical loads exceedances is thus fully in accordance with the concept. We built classes of
plots with throughfall N using the lower and upper values of empirical critical loads. However, throughfall N generally is lower than the total N deposition that also includes nitrogen
taken up in the crown. Hence, it is very likely that critical loads exceedances are more frequent than the >10 or >20 kg N ha-1 a-1 classes may suggest.
For the critical limit for soil solution that indicates N saturation, we investigated exceedance
based on the concentrations of inorganic nitrogen in soil solution samplers of the lowest
lysimeters. This was based on the assumption that the lowest sampler is installed more or less
at the in the lower end of the rooting zone and that the magnitude of variation is much higher
for the N concentration than for the water fluxes. However, for a more precise determination
(i) the effective depth of rooting zone should be crosschecked with the profile descriptions
and (ii) the leaching should be estimated with a water balance model.
About half of the plots with exceedance of critical loads for Nnut already show signs of N saturation and N leaching. It is likely that the other plots may still be in a phase of N accumulation
and tend towards saturation. However, we recommend investigating the N balances in a later
study.
Regarding critical loads of acidity (CLASMB), the proportion of samples with BC/Al ratio in
the soil solution of the mineral soil (20-80 cm depth) exceeding the critical limit (<0.8) tends
to be larger on plots with CLASMB exceeded than on other plots. As for nitrogen, it cannot be
stated that BC/Al is exceeded when CLASMB is exceeded. We note that the proportion of plots
with critical loads exceeded is relatively small (20-30%), while the BC/Al<0.8 criterion is
89
frequently exceeded in soil solution of the mineral soil on about two third of the plots, suggesting soils being acidified. However, we suggest that the proportion of toxic Al 3+ within
total dissolved Al be considered in further assessment of ecological risks. The relationship
between BC/Al ratio and foliage nutritional status was not very obvious: The Mg and K concentrations in foliage of conifers tend to be lower on plots with BC/Al ratio exceeded, indicating a possible depletion in base cations due to soil acidification. No such tendency was observed for broadleaves.
5.5 Conclusions
Most relations found in this investigation of the recent data of the ICP-Forests level II plots
from 2006 to 2009 support the hypothesis of on-going N saturation and acidification effects
due to atmospheric deposition of nitrogen:
 There is a clear relation between N-deposition and the occurrence of high nitrate concentrations below the rooting zone, which indicates that the plots are
in the phase of N saturation. About half of the plots with critical loads exceedance show such indication of N saturation. Further we estimate that the
other half of the plots with exceedance of critical loads for nutrient nitrogen are
still in the phase of accumulation and may show increasing effects in the future.
 There is also a clear relation between exceedance of critical loads of acidity
and occurrence of critical BC/Al ratios in soil solution that may indicate aluminium toxicity for plant roots.
 The relation between nitrate in soil solution and foliar nutrition status confirmed the tendency towards less optimal Mg/N ratios with increasing exceedance of critical limits of nitrate in soil solution for all major species
groups.
 For the species group ‘Pines’ there was a similar tendency between foliar nutrition and occurrence of critical BC/Al.
 Exceedances of critical limits for nitrate in soil solution were related to both
less favourable foliar nutrition and higher frequency of light to green discolouration, which is a typical symptom of e.g. nutrient deficiencies.
These findings support the critical loads concept. It has to be mentioned that relations between
variables cannot always be regarded as a proof of cause effects relationships. There might be
confounding factors. In this study, possible confounding by e.g. soil condition, tree age, tree
density, management and drought stress has not been investigated.
5.6 References
Aber, J. D., Nadelhoffer, K. J., Steudler, P. & Melillo, J. M., 1989: Nitrogen saturation in
northern forest ecosystems. Bioscience, 39 (6): 378-386.
Achermann, B. & Bobbink, R., 2003: Empirical Critical Loads for Nitrogen. Expert Workshop held under the Convention on Long-Range Transboundary Air Pollution covering the region of the UNECE, Berne, 11-13 November 2002. Environmental Documentation, Swiss Agency for the Environment, Forests and Landscape, Berne, 327 p.
Cronan, C. S., April, R., Bartlett, R. J., Bloom, P. R., Driscoll, C. T., Gherini, S. A., Henderson, G. S., Joslin, J. D., Kelly, J. M., Newton, R. M., Parnell, R. A., Patterson, H. H.,
90
Raynal, D. J., Schaedle, M., Schofield, C. L., Sucoff, E. I., Tepper, H. B. & Thornton,
F. C., 1989: Aluminum toxicity in forests exposed to acidic deposition - the Albios results. Water, Air, and Soil Pollution, 48 (1-2): 181-192.
de Vries, W., Reinds, G. J. & Vel, E., 2003: Intensive monitoring of forest ecosystems in
Europe: 2. Atmospheric deposition and its impacts on soil solution chemistry. Forest
Ecology and Management, 174 (1-3): 96-115.
Fischer, R., Lorenz, M., Köhl, M., Mues, V., Granke, O., Iost, S., Dobben, H. v., Reinds, G. J.
& Vries, W. d., 2010: The Condition of Forests in Europe. Executive Report. International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP-Forests, www.icp-forests.org). UN-ECE Convention on Longrange Transboundary Air Pollution (LRTAP), Federal Research Centre for Forestry
and Forest Products (BFH), Germany, 21 p.
Graf Pannatier, E., Thimonier, A., Schmitt, M., Walthert, L. & Waldner, P., 2011: A decade
of monitoring at Swiss Long-term Forest Ecosystem Research (LWF) sites: can we
observe trends in atmospheric acid deposition and in soil solution acidity? Environmental Monitoring and Assessment, 174: 3-30.
Granke, O. & Mues, V., 2010: Sulfur and nitrogen deposition and its trends. In: R. Fischer,
M. Lorenz, O. Granke, V. Mues, S. Iost, H. Van Dobben, G. J. Reinds & W. De Vries
(eds.), The Condition of Forests in Europe. 2010 Technical Report of ICP Forests.
UN-ECE Convention on Long-Range Air Pollution, International Co-opearative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICPForests) and European Commission, Hamburg, p. 45-53.
Gundersen, P., Callesen, I. & de Vries, W., 1998: Nitrate leaching in forest ecosystems is related to forest floor C/N ratios. In: Environmental Pollution, Vol. 102, pp. 403-407.
Gundersen, P., Schmidt, I. K. & Raulund-Rasmussen, K., 2006: Leaching of nitrate from
temperate forests - effects of air pollution and forest management. Environmental Reviews, 14: 1-57.
Gundersen, P., Sevel, L., Christiansen, J. R., Vesterdal, L., Hansen, K. & Bastrup-Birk, A.,
2009: Do indicators of nitrogen retention and leaching differ between coniferous and
broadleaved forests in Denmark? Forest Ecology and Management, 258 (7): 11371146.
ICP-Forests, 2010: Manual on methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests. International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP-Forests). Convention on Long-Range Transboundary Air Pollution
(LRTAP). UN-ECE., Hamburg, Prague, numerous p.
Iost, S., 2010: Soil soluton chemistry and its trends. In: R. Fischer, M. Lorenz, O. Granke, V.
Mues, S. Iost, H. Van Dobben, G. J. Reinds & W. De Vries (eds.), The Condition of
Forests in Europe. 2010 Technical Report of ICP Forests. UN-ECE Convention on
Long-Range Air Pollution, International Co-opearative Programme on Assessment
and Monitoring of Air Pollution Effects on Forests (ICP-Forests) and European Commission, Hamburg, p. 54-68.
Iost, S., Rautio, P., Lindroos, A.-J., Fischer, R. & Lorenz, M., 2011: Exceedance of critical
limits of nitrogen concentration in soil solution. In: R. Fischer & M. Lorenz (eds.),
The Condition of Forests in Europe. UN-ECE Convention on Long-Range Air Pollution, International Co-opearative Programme on Assessment and Monitoring of Air
Pollution Effects on Forests (ICP-Forests) and European Commission, Hamburg, p.
87-95.
91
Lorenz, M., Nagel, H.-D., Granke, O. & Kraft, P., 2008: Critical loads and their exceedances
at intensive forest monitoring sites in Europe. Environmental Pollution
Forests under Anthropogenic Pressure - Effects of Air Pollution, Climate Change and
Urban Development, 155 (3): 426-435.
MacDonald, J. A., Dise, N. B., Matzner, E., Armbruster, M., Gundersen, P. & Forsius, M.,
2002: Nitrogen input together with ecosystem nitrogen enrichment predict nitrate
leaching from European forests. Global Change Biology, 8 (10): 1028-1033.
Nagel, H.-D., Scheuschner, T., Schlutow, A., Granke, O., Clarke, N. & Fischer, R., 2011:
Exceedance of critical loads for acidity and nitrogen and scenarios for the future development of soil solution chemistry. In: R. Fischer & M. Lorenz (eds.), The Condition of Forests in Europe. UN-ECE Convention on Long-Range Air Pollution, International Co-opearative Programme on Assessment and Monitoring of Air Pollution
Effects on Forests (ICP-Forests) and European Commission, Hamburg, p. 97-113.
Nilsson, J. & Grennfelt, P., 1988: Critical loads for sulphur and nitrogen. Workshop in Skokloster, Sweden, 19-24 March 1988, Nordic Council of Ministers, Copenhagen, 418 p.
Pinheiro, J. & Bates, D., 2011: Linear and nonlinear mixed effects models (nlme). Package of
the R software for statistical computing on the CRAN repository (www.r-project.org):
334.
Spranger, T., Smith, R., Fowler, D., Mills, G., Posch, M., Hall, J., Schütze, G., Hettelingh, J.P. & Slootweg, J., 2004: Modelling and Mapping Critical Loads and Levels and Air
Pollution Effects, Risks and Trends. International Co-operative Programme for Modelling and Mapping. Convention on Long-range Transboundary Air Pollution (LRTAP).
UN-ECE, www.icpmapping.org, 236 p.
Stefan, K., Fürst, A., Hacker, R. & Bartels, U., 1997: Forest foliar condition in Europe - Results of large-scale foliar chemistry surveys (survey 1995 and data from previous
years). EC - UN/ECE, Austrian Federal Forest Research Centre, Brussels, Geneva,
207 p.
Sverdrup, H. & de Vries, W., 1994: Calculating critical loads for acidity with the simple mass
balance method. In: Water, Air, and Soil Pollution, Vol. 72, pp. 143-162.
Thimonier, A., Graf Pannatier, E., Schmitt, M., Waldner, P., Walthert, L., Schleppi, P., Dobbertin, M. & Kräuchi, N., 2010: Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Longterm Forest Ecosystem Research (LWF) sites? European Journal of Forest Research,
129 (3): 443-461.
92
6. National reports
Reports on the results of the national crown condition surveys at Level I of the year 2011
were received from 33 countries. For these countries, the present chapter presents summaries.
Besides that, numerical data on crown condition in 2011 were received. These results are
tabulated and presented as graphs.
It has to be noted, however, that in contrast to the transnational survey (Chapter 3) it is not
possible to directly compare the national survey results of individual countries. The sample
sizes and survey designs in national surveys may differ substantially and therefore conflict
with comparisons. In a number of cases the plots for the transnational survey are identical
with the national survey, in other cases the national survey is carried out on condensed nets.
Gaps in the Annexes II-1 to II-8, both tabulated and plotted, may indicate that data for certain
years are missing. Gaps also may occur if large differences in the samples were given e.g. due
to changes in the grid or the participation of a new country.
6.1. Andorra
The assessment of crown condition in Andorra in 2011 was conducted, as for last years, on
the 3 plots of the transnational grid, and included 72 trees, 42 Pinus sylvestris and 30 Pinus
uncinata.
The results obtained in 2011 continue to show an improving tendency in forest condition, as
registered the last 3 years, although the climate conditions were not as favourable as in these
previous years. These results show, for both species, a majority of trees classified in defoliation and discolouration classes 0 and 1.
Related to defoliation, it was registered an increase in not defoliated and slightly defoliated
trees, achieving each class the maximum rate since 2004 (63.9% and 27.8% respectively) and
a decrease in moderate defoliation class rate. There were no trees registered in severe defoliation class.
Results for discolouration showed an increase in not discolouration class and a decrease in
slight and moderate discolouration classes. Severe discolouration was not reported.
In 2011, the assessment of damage causes showed, as in previous surveys, that the main
causal agent was the fungus Cronartium flaccidum which affected 6.9% of the sampled trees
and which was distributed in all plots.
93
6.2. Belarus
The forest condition survey in 2011 was conducted on 417 Level I plots in the transnational
grid. The condition of 9 918 trees was assessed. 71.2% of all trees are coniferous (Pinus sylvestris and Picea abies, including Pinus sylvestris – 61.4%) and 28.8% are deciduous (Betula
spp., Populus tremula, Alnus spp., etc.).
Over last two years is observed an appreciable improvement of forest condition by sign defoliation. In comparison with 2009 the share of trees without defoliation increased by 2.7% and
was 30.5%, the share of trees of 2-4 classes decreased by 2.3% and was 6.1%. Average defoliation of all species was 16.6%.
As in previous years Alnus glutinosa has the least defoliation (57.5% not defoliated trees),
Fraxinus excelsior as in previous years had lowest average defoliation (10.5% not defoliated
trees). As a result of the degradation of ashen plantings observed since 2003, basically from
phytowreckers was lost about half of sampling trees. The average percent of sampling trees in
2011 was 49.5%.
Quercus robur in previous years had high average defoliation, but since 2006 obvious improvement of a condition is observed. In 2011 the share of trees without defoliation has increased to 29.6%, and the share of trees of 2-4 classes has decreased to 11.4%.
Damage signs were observed by various factors at 11.4% of the estimated trees. More often
damage occurred with Fraxinus excelsior (36.4%), Populus tremula (31.0%) and Quercus
robur (28.1%) and is rarer with Pinus sylvestris (8.0% of the estimated trees). Most often they
have been caused by fungi (4.5%), direct influence of the person (2.8%) and insects (1.3%).
The greatest share of trees with signs of damage by fungi is noted with Fraxinus excelsior
(36.4%, Armillaria sp.), Populus tremula (25.0%, basically Phellinus tremulae) and Quercus
robur (15.8%, basically Phellinus robustus). More often damaged mechanically are Betula
pendula (5.0%) and Picea abies (3.1%), by insects Alnus glutinosa (10.5%) and Quercus robur (3.0%).
Violent hurricanes continue to harm to the Belarus woods. Within the last years the destruction of stands from wind throw has essentially increased. Process of a dieback of spruce
stands essentially reduced in last years also definitively has not ended. In the south of the republic degradation of spruce stands continues.
6.3. Belgium
Belgium/Flanders
In 2011 the forest vitality network in Flanders did not change as compared to previous year.
The number of trees selected for crown condition assessment was 1 733 on 72 plots of the
regional 4 x 4 km grid. The main tree species are Quercus robur, Pinus sylvestris, Fagus sylvatica, Quercus rubra, Pinus nigra subsp. laricio and Populus spp.
Forest condition has slightly deteriorated in comparison to 2010. Overall 20.1% of the trees
were in defoliation classes 2-4; this is an increase with 4.0 percentage points. The mean defoliation was 22.0% and increased with 1.6 percentage points. A higher defoliation score was
registered both for conifers and broad-leaved trees. The share of trees with severe defoliation
was low (1.0%) and the mortality rate was 0.3%.
Broad-leaved tree species revealed a higher defoliation score than conifers. The main differences were noticed in the proportion of moderately to severely defoliated trees, which
amounts to 23.7% in broadleaves and 12.7% in conifers.
94
A high level of damage was observed in Populus spp., Quercus robur and a sample with
‘other broadleaves’, with 31.3%, 27.1% and 26.8% of the trees showing moderate to severe
leaf loss. The most affected species in the ‘other broadleaves’ sample were Alnus glutinosa,
Fraxinus excelsior and Betula pendula. The proportion of Fagus sylvatica and Pinus nigra
rated as damaged was 18.9% and 16.0% respectively. The least affected species were Pinus
sylvestris and Quercus rubra, with 11.7% and 6.5% of the trees in defoliation classes 2-4.
The deterioration in crown condition of Fagus sylvatica could be partly explained by a high
fructification. Common or abundant fruiting was recorded on 30.7% of the assessed trees.
Fruiting was also abundant in several Quercus robur plots. On 30.8% of the Q. robur trees
defoliators caused more than 10% leaf loss. There was an increase of damage by defoliators
on oaks during three consecutive years. The only species with an improvement of the crown
condition were Quercus rubra and Pinus nigra.
In several plots symptoms of Chalara fraxinea infection was assessed on natural regeneration
of Fraxinus excelsior. In 2011 the mean defoliation score of Common ash was 23.0% and
26.4% of the trees showed more than 25% defoliation.
The most important symptoms in the survey were devoured leaves (on 42.9% of the trees),
dead twigs or branches (42.1%), discolouration (red to brown: 25.6%, yellow: 18.8%) and
wounds (22.2%). Frequently noted causes were defoliators, powdery mildew and silvicultural
operations. Weather conditions were dry and sunny during spring and autumn but precipitation was high during summer. A summer storm caused serious damage in one transnational
16 x 16 km plot.
Belgium-Wallonia
The survey in 2011 concerned 861 trees on 40 plots, on a regional 8 x 8 km systematic grid.
The percentage of trees with a defoliation of 25% and more shows different long term trend
for conifers and broad-leaved.
The conifers, which were two times more defoliated in the beginning of the nineties, show
this year a rate of 22.8%, lower than last but higher than the last decade.
The broad-leaved showed an increase from 10% in 1990 to about 20% in 2005. These damages were mainly due to the degradation of the beech (scolytidae in 2000-2002, drought in
2003 followed by fruiting in 2004) and of the European oak (drought in 2003). The rate
dropped between 2006 and 2008 to 15.2%, but severely increased in 2009 to 32% and further
in 2010 to 33.4%. This year the rate is 32.4%.
Concerning the mean defoliation observed for the four main species, after an improvement
since 2006 for beech and European oak, they increase to about 25.9% for beech and for European oak this year. Sessile oak is at a bad condition this year with 17.3%. Spruce shows a decrease of mean defoliation, with 15.3% this year, 4.3% lower than last year.
Spring was very dry, with only 40% of normal rain, which could explain the high defoliation
for beech and European oak, the more sensitive species.
95
6.4. Bulgaria
In 2011 The Program for Large-Scale Monitoring of Forests in Bulgaria was carried out in
159 sample plots. The total of sampled trees was 5 583. The crown condition survey has been
performed in both coniferous and deciduous forests dominated by the species Pinus sylvestris
L., Pinus nigra Arn., Picea abies (L.) Karst. и Abies alba Mill., Fagus sylvatica L., Quercus
frainetto Ten., Quercus petraea (Matt.) Liebl., Quercus cerris L., , Quercus rubra L., Tilia
platyphillos Scop.and Carpinus betulus L.. The number of the coniferous trees was 2 397 and
the number of deciduous trees 3 186.
Concerning the crown condition survey results the major part 78.42% of the sampled trees is
slightly defoliated (up to 25%). Defoliation class 2 has 17.89% of the sampled trees in 2011
survey. In comparison with the crown condition in 2010 the results of this year showed that
the trees with defoliation class 0 and 1 have increased by 2.20%. The moderately defoliated
trees decreased by 4%. The share of dead trees increased from 0.45% in 2010 to 2.10% in
2011.
The crown condition of the deciduous trees in 2011 showed better results than in the previous
year. As a whole, the results for the observed deciduous trees showed a prevalence of the
healthy and slightly affected by defoliation trees. Healthy and slightly defoliated had been
92.80% of the observed trees of Fagus sylvatica, 86.17% of Quercus cerris trees and 85.55%
of Quercus frainettoI. The highest percentage of deciduous dead trees is observed in oak
trees: Qercus cerris (5.11%) and Quercus petraea (3.10%). The damages on Fagus sylvatica
were due to Rhynchaenus fagi, Ectoedemia libwerdella, Nectria spp., and the damages on
Querqus spp. were caused mainly by Tortrix viridana.
Abies alba had the best condition of the sampled coniferous trees, followed by Picea abies. A
tendency toward deterioration in coniferous sampled trees as a whole was not determined.
Most of the damages for conifers species were caused by Lophoderminium pinastri.
In 2011 a negative influence in crown conditions both for conifers and deciduous trees was
registered caused by abiotic agents such as drought, snow, ice and anthropogenic agents as
well.
6.5. Croatia
In the forest condition survey in 2011, 92 sample plots on the 16 x 16 km grid network were
included.
The percentage of trees of all species within classes 2-4 in 2011 (25.2%) was lower than in
2010 (27.9%), which was highest in the last ten years. The share of broadleaves in classes 2-4
(21.5%) was also somewhat lower than in 2010 (21.8 %). For conifers, the percentage of trees
in classes 2-4 was 45.1%, which is the lowest score since the year 1996. There were 350 conifer trees and 1858 broadleaves in the sample (272 conifer trees vs. 1744 broadleaves in survey
2010).
Abies alba is the most defoliated tree species in Croatia (78.0% trees in classes 2-4) in 2011,
followed by Pinus nigra (52.9% trees in classes 2-4). The percentage of moderately to severely damaged Silver fir trees recorded in 2010 was 66.1% and 72.2% in 2009. The lowest
value, 36.6% of moderately to severely damaged trees was recorded in 1988, whereas in 1993
the share was already 70.8%. In the year 2001 it reached 84.5 %, and after a slight decrease in
2002 (81.2%), the trend of increasing defoliation was continued with 83.3% of moderately to
96
severely damaged trees in 2003, 86.5% in 2004 and 88.5 % in the year 2005. After that, the
values were lower (70.8% in 2006, 67.9% in 2007 and 70.1% in 2008).
The lowest percentage of Quercus robur trees in classes 2-4 was recorded in 1988 (8.1%), the
highest in 1994 (42.5%), and it has been fairly constant later at around 25-30% until the year
2000. Afterwards it decreased to values below 20% (15.4% in 2003, 18.5% in 2004). In 2005
a slight increase was recorded with 22.1% of moderately to severely defoliated oak trees. In
2006 it was slightly lower at 20.5%, in 2007 it was again lower at 19.6%, returning to values
above 20% in 2008 (22.2%), 2009 (22.8%) and 2010 (26.0%) of Pedunculate oak trees in defoliation class 2-4. This year the value is somewhat lower at 22.3%.
Although the maximum percentage of moderately to severely defoliated beech trees was recorded this year (13.2%), Fagus sylvatica remains one of the tree species with lowest defoliation. In the last ten years of monitoring, the percentage of Common beech trees in classes 2-4
varied from 5.1% in 2003 to 12.3% in year 2001.
In summary, crown defoliation of all species, broadleaves and conifers, has improved in 2011,
but the status of some important species, such as beech and fir, has deteriorated.
6.6. Cyprus
The annual assessment of crown condition was conducted on 15 “Level I” plots during the
period September - October 2011. The assessment covered the main forest ecosystems of Cyprus and a total of 360 trees (Pinus brutia, Pinus nigra and Cedrus brevifolia) were assessed.
Defoliation, discoloration and the damaging agents were recorded.
A comparison of the results of the conducted survey with those of the previous year (2010)
shows slight improvement among the four categories on all species.
From the total number of trees assessed (360 trees), 12.5% of them were not defoliated,
71.1% were slightly defoliated, 15.8% were moderately defoliated, and 0.6% were severely
defoliated.
A comparison with the results of the previous year, 2010 results show an increase in the first
two classes, 0.3% in class 0 (not defoliated) and 2.5% in class 1 (slightly defoliated). A decrease of 1.9% in class 2 (moderately defoliated) and a decrease of 0.8% in class 3 (severely
defoliated) have been observed. No dead trees have been recorded (class 4, Dead). The observed improvement of crown in 2011 is mainly due to the sufficient rainfall of the period
2008 - 2010 comparing to the rainless period, 2007 - 2008.
In the case of Pinus brutia, 14.0% of the sample trees did not show any defoliation, 69.3%
were slightly defoliated, 16.0% were moderately defoliated and 0.7% severely defoliated. For
Pinus nigra, 5.6% of the sample trees did not show defoliation, 72.2% showed slight defoliation while the rest 22.2% were moderately defoliated. Also Cedrus brevifolia, 4.2% of the
sample trees did not show defoliation, 91.7% were slightly defoliated and 4.2% were moderately defoliated. No dead trees have been observed.
From the total number of trees assessed (360 trees), 100% of them were not discoloured.
48.1% of the total number of sample trees surveyed showed signs of insect attacks and 17.2%
showed signs of attacks by “other agents, T8” (lichens, dead branches and rat attacks). Also
8.3% showed signs of both factors (insect attacks and other agent).
97
6.7. Czech Republic
In 2011 no pronounced change in the development of total defoliation was recorded in the
older age category of conifers (60-years-old stands and older) compared to the preceding year.
No pronounced changes occurred in the particular tree species in this age category. Compared
to the preceding year, a very moderate decrease in the trend of total defoliation was observed
in the younger age category of conifers (stands younger than 59 years) in 2011, when the percentage of conifers in defoliation class 0 slightly increased while it decreased in defoliation
class 1. This slight decrease in defoliation occurred in all observed main coniferous species
(Picea abies, Abies alba and Larix decidua) with the exception of pine (Pinus sylvestris),
which showed a moderate upward trend of defoliation for several years.
Similarly like in the same age category of conifers, no pronounced change in the development
of total defoliation of broadleaves was recorded in the older age category (60-years-old stands
and older). Among the particular tree species, a moderate increase in defoliation was observed
only in beech (Fagus sylvatica) due to a moderate decrease in its percentage in defoliation
class 1 at a simultaneous increase in its percentage in defoliation class 2. Younger broadleaves
(stands younger than 59 years) showed a moderate decrease in total defoliation similarly like
the same age category of conifers. Their percentage in defoliation class 0 increased from
21.0% in 2010 to 28.3% in 2011 at a simultaneous decrease in their percentage in defoliation
class 1 and 2. The less represented tree species – birch (Betula pendula) contributed to this
positive change in younger broadleaves to the greatest extent as its percentage in defoliation
class 0 increased very significantly.
Younger conifers (less than 59 years) show lower defoliation in the long run than the stands
of younger broadleaves. In older stands (60-years-old and more) such a comparison reveals an
opposite trend: older conifers have markedly higher defoliation than the stands of older broadleaves. In both age categories the pine crucially contributes to a higher percentage of defoliation in the group of conifers.
At the beginning of the growing season in May some stands (mainly the beech ones) had been
damaged to a greater extent by late frost in the stage of flushing. In many cases subsequent
flushing from secondary buds failed to fully compensate for the loss of assimilatory tissues.
The imbalance of weather conditions (the temperature to precipitation amount ratio) also had
adverse effects on the regeneration of damaged crowns. In comparison with the long-term
normal temperatures, the average of the monthly temperatures in the growing season was
mostly above average (deviation of +3.2° in April). The temperature was below average only
in July and October (very moderately). On the contrary, in this comparison the average
monthly precipitation amount was mostly below average (at the lowest in April with 74%)
and it was above average only in July and October.
The emissions of the main pollutants (particulate matter, SO2, NOx, CO, VOC, NH3) did not
show any pronounced change within the last ten years while total emissions of the majority of
these pollutants have moderately decreased in the long run in spite of some fluctuations and
the emissions of particulate matter and NH3 have been constant.
6.8. Denmark
The Danish forest condition monitoring in 2011 was carried out in the National Forest Inventory (NFI) and on the remaining Level I and Level II plots. Monitoring showed that most tree
species had satisfactory health status. Exceptions were Fraxinus excelsior in which the problem with extensive dieback of shoots continued. Average defoliation was 28% for all monitored ash trees, and 42% of the trees had more than 30% defoliation. In some ways even these
98
data do not completely reflect the situation because most of the severely diseased ash stands
are clear cut or abandoned. Thus the only national long term ash monitoring plot in Denmark
was finally discontinued in 2011 because the trees were dying and falling over after two years
of defoliation scores above 90%.
Picea sitchensis and Pinus sylvestris have recovered and are back to a low average defoliation
(8% and 9% respectively). Picea abies and other conifers had low defoliation, and the health
situation for P. abies in Denmark is still very satisfactory with an average defoliation of only
6%. The average defoliation score of Fagus sylvatica increased to 11.4%, but this is partly
due to the fact that 2011 was a mast year. Quercus (robur and petraea) stayed at a slightly
increased defoliation, but even with 16% average defoliation the health condition of oak can
be considered satisfactory. The growth season in Denmark was dry in spring but very wet
during summer, which was a benefit for most of the forest stands except those suffering from
high water levels.
Based on both NFI plots and Level I & II plots, the results of the crown condition survey in
2011 showed that 79% of all coniferous trees and 58% of all deciduous trees have been undamaged. 18% of all conifers and 29% of all deciduous trees showed warning signs of damage. The mean defoliation of all conifers was 7% in 2010, and the share of damaged trees was
only 3%, which is an improvement since last year. Mean defoliation of all broadleaves was
14% and 13% were damaged, which is worse than 2010, but most of the increase was due to
ash dieback and a few declining oak trees.
6.9. Estonia
Forest condition in Estonia has been systematically monitored since 1988. In 2011 altogether
2,372 trees, thereby 1,489 pines Pinus sylvestris, 582 spruces Picea abies and 227
birches Betula pendula, were examined on 98 permanent Level I sample plots from July to
October.
The total share of not defoliated trees, 50.8%, was 2% lower than in 2010. Percentage of trees
in classes 2 to 4 (moderately to dead) was 8.0%.
Percentage of conifers in classes 2 to 4 (moderately to dead) was in 2011 8.7%. In Estonia
the most defoliated tree species have traditionally been Scots pine (Pinus sylvestris), but no
considerable changes compared to 2010 happened and number of pine trees in all defoliation
classes remained almost at the same level. Some increase of defoliation of Norway spruce
(Picea abies) occurred, the share of not defoliated trees (defoliation class 0) dropped by 5 %.
Percentage of broadleaves in classes 2 to 4 (moderately to dead) was in 2011 3.0%. No considerable changes compared to 2010 happened.
Numerous factors determine the condition of forests. Climatic factors, disease and insect
damage as well as other natural factors have an impact on tree vitality.
In 2011 67.2% of all trees assessed had some type of visible damage; thereby 8.0 % of the
trees had some kind of insect damages and 44% identifiable symptoms of disease. Needle
shedding and shoot blight were as usual the most significant reasons of biotic damage of trees.
99
6.10. Finland
The large scale crown condition survey (Level I) has been carried out in Finland on a systematic network of permanent sample plots for 25 years, since 1986. Before 2009 a subsample of
the permanent plots established during 1985–1986 in connection with the 8th National Forest
Inventory (NFI).
The integration between ICP Level 1 and NFI was accomplished in 2009 in Finland. The
sampling design of the current NFI is a systematic cluster sampling. The distance between
clusters, the shape of a cluster, the number of field plots in a cluster and the distance between
plots within a cluster vary in different parts of the country according to spatial variation of
forests and density of road network. Principally, every fourth cluster is marked as a permanent
cluster. Annually, a new set of permanent plots, established during the 9th NFI in 1996-2003,
is assessed in the forest condition survey. The trees are sampled by the relascope
Tallied dominant and co-dominant Norway spruce, Scots pine and Birch trees from six preselected permanent plots from each cluster are assessed. The same permanent plots will be
assessed in five-year intervals. In 2009-2010 all trees were assessed, but in 2011, owing to
limited resources, a maximum of six trees per appropriate species were included in the sample, resulting in a reduced number of assessed trees.
Please note that because the plots assessed during 2009 -2011 are completely different samples, the results are not directly comparable with each other or with the results of the previous
years.
The results of the 2011 forest condition survey are reported from preselected 717 permanent
sample plots. Of the 4,147 trees assessed in 2011, 51.7% of the conifers and 62.2% of the
broadleaves have not been defoliated (leaf or needle loss 0-10%). The proportion of slightly
defoliated (11- 25%) conifers was 36.7% and that of at least moderately defoliated (over 26%)
11.7%. For broadleaves the corresponding proportions were 31.7 and 5.6%, respectively.
The average tree-specific degree of defoliation was 14.3% in Scots pine, 16.7% in Norway
spruce, and 12.4% in broadleaves (Betula pendula and B. pubescens). Compared to the previous year, the mean defoliation of Scots pine in 2011 was higher, but the defoliation of the
other species was less in 2011 than in 2010.
Abiotic and biotic damage was also assessed in connection with the large scale monitoring of
forest condition. 31% of the Scots pines, 28% of the Norway spruces and 23% of the broadleaves were reported to have visible symptoms attributed to abiotic or biotic damaging agents.
The number of symptomatic trees was almost at the same level (29%) as in the previous year.
The proportion of insect and abiotic damage was slightly smaller, but the number of unidentified damage much larger than in 2010. Apart from physical contact, pine sawflies, mostly
Neodiprion sertifer, common pine shoot beetles (Tomicus sp.) and Gremmeniella abietina
were the most abundant identified damaging agents in Scots pine. Neodiprion sertifer was
having a massive outbreak in the mid-western parts of the country, but the amount of damaged pines was slightly less (4.1%) than in the previous two years, in the nation-wide data.
6.11. France
In 2011, the forest damage monitoring in the French part of the systematic European network
comprised 11 352 trees on 554 plots. The increase in plot number is due to a will to take into
account the increasing forest area in France for several years.
100
The climatic conditions of the year have not been really favourable to the forest vegetation
due to a particularly dry spring. Nevertheless, a wet and chilly summer impeded to see high
levels of defoliation among network’s trees.
The foliage slightly increased for most of broadleaved species and even conifers. Quercus
pubescens and evergreen oak, species which are frequent in the South East of France, still had
the worst crown condition of all monitored species in 2011 and did not show any sign of improvement.
Death of sampled trees stayed at a relatively low level. The number of discoloured trees was
still low except for poplars, beech, wild cherry and Aleppo pine.
Damage was reported on about a quarter of the sampled trees, mainly on broad-leaved species. The most important causes of damage were mistletoe (Viscum album) on Pinus sylvestris, chestnut canker (Cryphonectria parasitica) and the oak buprestid (Coroebus florentinus)
on Quercus spp. Abnormally small leaves were observed on different species, especially on
Quercus spp. (mainly on evergreen and pubescent oaks).
6.12. Germany
The forest condition deteriorated in 2011 as compared with 2010. In particular beech trees
show significantly worse crown condition than they did in 2010. This is however mainly due
to intense fruiting. Spruce and Scots pine are nearly unchanged if the national average is considered. Oak trees have recovered.
The national result 2011 was calculated based on the crown condition data of 10 167 sample
trees which were assessed on 410 sampling plots of the national 16 km x 16 km grid. The assessment covers 38 different tree species. About 80% of all trees included in the samples belong to the four main tree species: spruce (Picea abies), Scots pine (Pinus sylvestris), beech
(Fagus sylvatica) and deciduous temperate oak (Quercus robur and Quercus petraea are assessed together). The remaining tree species are grouped as “other conifers” and “other broadleaves”.
Over all tree species, 28% (2010: 23%) of the forest area was assessed as damaged, i.e. showing more than 25% of defoliation (damage classes 2 to 4). 35% were at the warning stage and
37% were undamaged (2010: 38%). Mean crown defoliation increased from 19.1% to 20.4%.
Picea abies: the area percentage of damaged trees is 27% (2010: 26%). The percentage without crown defoliation is 40%, the same as in the previous year. Mean crown defoliation increased slightly from 18.7% to 19.1%.
Pinus sylvestris: the area percentage of damaged trees is 13% and remains unchanged. 45%
(2010: 44%) did not show defoliation. Mean crown defoliation slightly decreased from 16%
to 15.6%.
Fagus sylvatica: the area percentage of damaged trees is 57%, even more than in 2004. In
comparison to the previous year (2010: 33%) this is an increase by 24 percentage points. Only
12% of the trees did not show defoliation (2010: 20%). Mean crown defoliation increased
from 23.3% to 30.4%, a level similar to the one reached in 2004. This is mainly due to a prolific mast year. Fruiting was recorded on more than 90% of all beech trees older than 60 years.
For beech trees there is a strong relationship between fruiting and crown defoliation. Fruiting
needs a lot of resources at the expense of the growth of foliage and wood. Furthermore beech
trees might have suffered from the scarcity of water in spring and early summer.
101
Quercus petraea and Q. ruber recovered compared with the previous year. The area of damaged trees decreased by10 % points from 51% to 41%. 21% of the oaks were classified as
undamaged. Mean crown defoliation decreased from 29.6% to 26.3%. Damage caused by
defoliators has decreased and there were only few mildew infections.
6.13. Hungary
In 2011 the forest condition survey – based on the 16 x 16 km grid – included 1 830 sample
trees on 78 permanent plots in Hungary (two of them are temporarily un-stocked). The assessment was carried out between 15th July and 15th August. 86.7% of all assessed trees were
broadleaves (a little increasing during the last years), 13.3% were conifers.
Overall health condition of the Hungarian forests compared to previous year became better.
Although the share of trees without visible damages decreased from 49.3% to 45.9%, the
mean defoliation of all species was 15,8%. This is more than 5 percentage point lower than in
2010.
The percentage of all trees within ICP defoliation classes 2-4 (moderately damaged, severely
damaged and dead) in 2011 (18.9%) is lower than in 2010 (21.8%). In Hungary the dead trees
remain in the sample till they are standing, but the newly (in the surveyed year) dead trees can
be separated. In 2011 the rate of newly dead trees was 1% of all trees that is by 0.7% higher
than in the previous year. The number of all dead trees increased just a little.
In the classes 2-4 the most damaged species are Pinus nigra (36.2%), Robini pseudoacacia,
(29.0%), the Pinus silvestris (26.3%). These percentages show the rate of sample trees belonging to category 2-4). Carpinus betulus had the lowest defoliation (8.9%) in class 2-4.
Generally all species’ rate decreased in these categories, especially the Pinus nigra (almost
20%).
Discoloration can be rarely observed in the Hungarian forests, 94.3% of sample trees did not
show any discoloration, compared to the previous year the change is less than 1% in all categories.
According to the classification defined in the ICP manual on crown condition the damage
caused by defoliator insects had the biggest rate, 29.0% of all the damages. This damage occurred especially on the following species: Quercus robur, Quercus petraea and other Quercus species and Pinus Silvestris. The mean damage values of these trees were 6.1%, 6.6%,
7.7%, 12.6%.
The rate of assessed damages attributed to fungus was 17.0%. Fungal damages assessed on
leaves were 4.5%, on branch and on stem together were 12.5% of all assessed trees. The mean
damage value was 19.0%. 26.4% of the assessed damages were abiotic. The most important
identifiable causes are draught (22.3%), frost (13.9%) and wind-breaking (7.7%).
6.14. Italy
The survey of Level I in 2011 took into consideration the condition of the crown by 8,099
selected trees in 253 plots belonging to the EU network 16 x 16 km. The number of sample
areas has compared to the survey of 2010. The results given below relate to the distribution of
frequencies of the indicators used, especially transparency - which in our case we use for the
indirect assessment of defoliation and the presence of agents known causes attributable to
both biotic and abiotic. For the latter, not so much the indicators we analysed the frequencies
102
of affected plants, but the comments made as to each plant may have multiple symptoms and
more agents.
Defoliation data are reported according to the usual categorical system (class 0: 0-10%; class
1: >10-25%; class 2: >25-60%; class 3: >60%; class 4: tree dead): most (73.0%) is included in
the classes 1 to 4; the 31.3% is included in the classes 2 to 4.
By analysing the sample for groups of species, conifers and broadleaves, it appears that conifers have a transparency of less than deciduous foliage: 33.7% of conifers and 24.4% of
broadleaves are without any defoliation (Class 0).
The conifers falling in the defoliation classes 2 to 4 are 27.8% respect to the 32.7% of broadleaves.
From a survey of the frequency distribution of the parameter for transparency species divided
into two age categories (<60 and 60 years), among the young conifers (<60 years), Pinus
sylvestris is the 35.5% of trees in the classes 2 to 4, followed by Larix decidua (23.6%) and
Picea abies (21.5%), Pinus nigra (20.4%), but the best conditions of trees in the classes 2 to
4, there is on the Pinus halepensis with the 4.5%.
Among the old conifers (60 years), with the species appear to be worse quality of foliage on
Pinus sylvestris (66.6%), Picea abies (37.6%), Larix decidua (26.1%), Abies alba (24.8%) of
trees in the classes 2 to 4, while Pinus cembra (17.6%) to be the conifer is in better condition
of trees in the classes 2 to 4.
Among the young broadleaves (<60 years), Castanea sativa and Quercus pubescens have
respectively 70.2% and 50.0% of trees in the classes 2 to 4, while others have a frequency
range between 15.4% and 25.1% in classes 2 to 4 distributed in different species: Quercus
cerris (15.4%), Fagus sylvatica (21.7%), and Ostrya carpinifolia (25.1%).
Among the old broadleaves (60 years) in the classes 2 to 4, Castanea sativa has (56.2%),
Quercus pubescens (42.6%), Fagus sylvatica (15.6%), Quercus ilex (14.0%) and Ostrya
carpinifolia (13.7%) has the lowest level of defoliation of trees in the classes 2 to 4.
Starting from 2005, a new methodology for a deeper assessment of damage factors (biotic and
abiotic) was introduced. The main results are summarized below.
Most of the observed symptoms were attributed to insects (22.3%), subdivided into defoliators (16.4%), aphids (2.1%), wood borers (1.1%), needle miners (1.0%), following symptoms
attributed to fungi (6.6%) the most significant are attributable to “dieback and canker fungi”
(3.2%), then those assigned to abiotic agents, the most significant are “drought/aridity”
(1.1%) and the “hail” (1.0%).
6.15. Latvia
The forest condition survey 2011 in Latvia was carried out in parallel on two plot sets: on the
ICP Level I on the grid of 8 × 8 km, totally 288, including 88 plots on the transnational grid
16 × 16 km, and on recently established NFI plots, totally 115. The transition process to the
NFI system is still ongoing, therefore the national report of 2011 is based on the ICP Level I
plot data.
In total, on Level I plots 6,644 trees were assessed, of which 72% were conifers and 28%
broadleaves. Of all tree species, 13.8% were not defoliated, 72.2% were slightly defoliated
and 14.0% moderately defoliated to dead. Comparing to 2010 no considerable changes were
observed in the distribution in these classes. The proportion of trees in defoliation classes 2-4
remained to be about 5-7% higher for conifers than for broadleaves.
103
Mean defoliation of Pinus sylvestris was 22.4% (21.8% in 2010). The share of moderately
damaged to dead trees constituted 16.4% (15.4% in 2010). Slight increase in the defoliation
level is observed for Pinus sylvestris during the recent years. Mean defoliation of Picea abies
was 20.7% which is only 0.3 % points higher than in 2010. Changes in the distribution of
trees in defoliation classes are insignificant for Picea abies as well. The mean defoliation
level of Betula spp. was 18.0% in 2011, showing insignificant decrease of the defoliation
level during the last five years. The share of trees in defoliation classes 2-4 decreased to 8.3%.
The worst crown condition of all assessed tree species remained for Fraxinus excelsior with
mean defoliation 31.8% and the share in defoliation classes 2 to 4 of 41%. It must be mentioned that these results are based on a comparatively low tree number of Fraxinus excelsior
in the survey.
Visible damage symptoms were observed to a similar extent than in the previous year –
12.2% of the assessed trees (11.3% in 2010). Most frequently recorded damages were caused
by abiotic factors (21.3% of all cases), direct action of man (17.7%) and fungi (15.1%). The
proportion of insect damages has decreased considerably during the last two years. The greatest share of trees with damage symptoms was recorded for Populus spp.
In winter 2010/2011 considerable damage of tree stands in the eastern part of Latvia was
caused by freezing rain and snow, breaking tree tops and branches of conifers as well as
broadleaves.
An outbreak of Lymantria monacha was observed in the vicinity of Riga city in 2011, considerably defoliating about 2000-3000 ha of Pinus sylvestris stands. It is expected that the maximum of the outbreak will be reached in 2012.
6.16. Lithuania
The national forest inventory and the regional forest health monitoring grids (4 × 4 km) in
Lithuania are combined since 2008. The transnational grid of Level I (16 × 16 km) plots was
kept unchanged and the monitoring activities were continued. In 2011 the forest condition
survey was carried out on 1 009 sample plots from which 77 plots were on the transnational I
Level grid and 932 plots on the national forest inventory grid. In total 5,738 sample trees representing 18 tree species were assessed. The main tree species assessed were Pinus sylvestris,
Picea abies, Betula pendula, Betula pubescens, Populus tremula, Alnus glutinosa, Alnus incana, Fraxinus excelsior, Quercus robur.
The mean defoliation of all tree species slightly decreased to 21.2% (22.6% in 2010). 15.6%
of all sample trees were not defoliated (class 0), 69.1% were slightly defoliated and 15.3%
were assessed as moderately defoliated, severely defoliated and dead (defoliation classes 2-4).
Mean defoliation of conifers was 21.1% (22.0% in 2010) and for broadleaves 21.3% (23.4%
in 2010).
Mean defoliation of Pinus sylvestris was 21.4% (21.5% in 2010). Starting from 1998 mean
defoliation of Pinus sylvestris has not exceeded 22.0%. The number of trees in defoliation
classes 2-4 was not changed and was 16.0% as in 2010. Mean defoliation of Picea abies decreased to 20.1% (22.9% in 2010) and the share of trees in defoliation classes 2-4 distinctly
decreased and was 16.9% (28.8% in 2010).
Populus tremula had the lowest mean defoliation and the lowest share of trees in defoliation
classes 2-4. Mean defoliation of Populus tremula was 17.3% (19.3% in 2010) and the proportion of trees in defoliation classes 2-4 was only 4.4% (14.% in 2010). Mean defoliation of
Alnus glutinosa decreased to 19.4% (23.4% in 2010) and the share of trees in defoliation
classes 2-4 – to 9.5% (25.0% in 2010). In 2009 – 2010 the condition of Alnus glutinosa was
104
the worst in the whole observation period (1989 – 2011). Mean defoliation of Alnus incana
decreased to 22.1% (25.1% in 2010). The share of trees in defoliation classes 2-4 noticeable
decreased to 13.7% (28.3% in 2010). Mean defoliation of Betula spp. decreased to 19.9%
(21.5% in 2010) and the share of trees in defoliation classes 2-4 decreased to 12.7% (19.6% in
2010).
The condition of Fraxinus excelsior remained the worst between all observed tree species.
This tree species had the highest defoliation since 2000. In 2007 – 2008 mean defoliation of
Fraxinus excelsior has been gradually decreasing, but increased again in 2009 - 2011. The
assessed mean defoliation was 43.5% (41.2% in 2010). The share of trees in defoliation
classes 2-4 increased up to 58.3% (55.6% in 2010). Mean defoliation of Quercus robur decreased to 21.9% (25.4% in 2010) and the number of trees in defoliation classes 2-4 decreased
to 15.7% (24.8% in 2010).
20.7% of all sample trees had some kind of identifiable damage symptoms. The most frequent
damage was caused by abiotic agents (5.4%), direct action of man (4.4%) and fungi (4.3%).
The highest share of damage symptoms was assessed for Fraxinus excelsior (54.2%), Populus
tremula (34.2%) and Alnus incana (33.5%), the least for Alnus glutinosa (12.6%) and for
Pinus sylvestris (16.1%).
In general, the condition of Lithuanian forests is better in 2011 than in 2010. However, mean
defoliation of all tree species has varied inconsiderably from 1997 to 2011 and the condition
of Lithuanian forests can be defined as relatively stable.
6.17. Republic of Moldova
In 2011 forest condition survey was carried out on 567 plots with a grid of 2 × 2 km. A total
number 12 552 sample trees was assessed, including 78 trees of coniferous species and 12 444
trees of broadleaves species.
Climatic conditions at the beginning of the vegetation period were favourable for the growing
and the development of arboreal and shrubby vegetation. But in the second half of the year
and till the end of vegetation on the territory of the country were observed dry conditions. In
spite of this, sanitary condition of plantations in general was insignificantly improved in comparison with the previous year. Thus, share of trees of 2-4 defoliation class decreased with
4.1% comparably with last year and in 2011 makes 18.4% against 22.5% in 2010.
Almost for all species of broadleaves and conifers the decrease in share of trees of the 2-4
defoliation class it is observed, or it remains at the level of the last year. In oak stands the
quantity of trees of the 2-4 defoliation class decreased by 6.2% comparably to the last year
and amounted to 19.6%. In the conifer stands this group decreased by 1.2% and amounted to
32.1% this year. In the acacia stand trees of the 2-4 defoliation class made 36.4%, which is on
3.3% less than last year.
Only for elm species an increase of this index for 2.1% as compared to the last year can be
observed and it constitutes 40.5% for 2011. This can be explained with the intensive crown
grazing by Aprocerus leucopoda during several years.
105
6.18. Norway
The results for 2011 show a small increase in crown defoliation for all tree species compared
to the year before. The mean defoliation for Picea abies was 15.5%, Pinus sylvestris was also
15.5%, and for Betula spp. 23.6%. After a peak in 2007 with high defoliation for all of the 3
monitored tree species Norway spruce, Scots pine and birch, and then a decrease in defoliation in the following years (2008-2010), this last year 2011 again shows an increase in the
defoliation of these tree species. During the last ten years birch had the lowest defoliation in
2001, while 2011 is the year with the third highest defoliation. Norway spruce and Scots pine
show only minor changes in defoliation over the last four years (2008-2011).
Of all the coniferous trees, 47.1% were rated as not defoliated in 2011, which is a decrease of
about 4% points as compared to the year before. Only 38.7% of the Pinus sylvestris trees were
rated as not defoliated which is a decrease with 2% points. 53.0% of all Norway spruce trees
were not defoliated, a decrease with 4% points compared to the year before. For Betula spp.
21.0% of the trees were observed in the class not defoliated, also representing a decrease with
8.6% points compared to the year before. For birch trees, especially the class ‘moderately
defoliated’ increased from 21.7 to 26.9% in 2011. For other classes of defoliated trees, only
small changes were observed.
In crown discolouration we observed 11.6% discoloured trees for Picea abies, an increase
with 2.3% points from 9.3% in 2010. For Pinus sylvestris, only 3.8% of the assessed trees
were discoloured, an increase with about 1% points from the year before. For Betula spp., the
discolouration followed up the increase from 2010 and was now 11.4% in 2011. For birch, the
observed trees in the most serious class ‘Severely discolouration’ were 3-doubled from 1.0%
in 2010 to 3.3% in 2011.
The mean mortality rate for all species was 0.3% in 2011. The mortality rate was 0.4%, 0.2%
and 0.2% for spruce, pine and birch, respectively. The mortality rate of birch has been more
normal the last three years and is heavily reduced from the high level of 1-2% which occurred
in the three year period 2006-2008 probably due to serious attacks of insects and fungi.
In general, the observed crown condition values result from interactions between climate,
pests, pathogens and general stress. According to the Norwegian Meteorological Institute the
summer (June, July and August) of 2011 was regarded as warmer than normal (1.2 ºC
warmer) and with 40% more precipitation as normal as an average for the whole country. In
south-east Norway, where summer drought is a frequent problem for trees, the precipitation in
2011 was the highest ever measured and was 95% higher than normal. The second highest
precipitation was in 1964 with 65% more precipitation than normal. There are of course large
climatic variations between regions in Norway which range from 58 to 71 ºN.
6.19. Poland
In 2011 the forest condition survey was carried out on 1 947 plots. Forest condition was
worse than in the previous year. 14.0% of all sample trees were without any symptoms of
defoliation, indicating a decrease by 7 percent points compared to 2010. The proportion of
defoliated trees (classes 2-4) increased by 3.3 percent points to an actual level 24.0% of all
trees. The share of trees defoliated more than 25% increased by 4.0 percent points for conifers
and by 2.0 percent points for broadleaves.
11.3% of conifers were not suffering from defoliation. For 24.2% of the conifers defoliation
of more than 25% (classes 2-4) was observed. With regard to the three main coniferous species Abies alba remained the species with the lowest defoliation, although indicated a worsening comparing to previous year. A share of 17.3% (11.4% in 2010) of fir trees up to 59 years
106
old and 16.1% (15.5% in 2010) of fir trees 60 year old and older was in defoliation classes 24.
19.1% of assessed broadleaved trees were not defoliated. The proportion of trees with more
than 25% defoliation (classes 2-4) amounted to 23.5%. As in the previous survey the highest
defoliation amongst broadleaved trees was observed in Quercus spp. although indicated slight
improvement in older stands. In 2011 a share of 28.0% (28.5% in 2010) of oak trees up to 59
years old and 32.2% (37.8% in 2010) of oak trees 60 years old and older was in defoliation
classes 2-4. Fagus sylvatica remained the broadleaves species with the lowest defoliation,
although indicated a worsening comparing to previous year. A share of 9.8% (7.6% in 2010)
of beech trees up to 59 years old and 11.8% (7.4% in 2010) of beech trees 60 year old and
older was in defoliation classes 2-4.
In 2011 discolouration (classes 1-4) was observed on 2.8% of the conifers and 2.3% of the
broadleaves.
6.20. Romania
In the year 2011, the assessment of crown condition on Level I plots in Romania was carried
out on the 16 x 16 km transnational grid net, during 15th of July and 15th of September. The
total number of sample trees was 5 808, which were assessed on 242 permanent plots. From
the total number of trees, 1104 were conifers and 4704 broadleaves. Trees on 12 plots were
harvested during the last year and several other plots were not reachable due to natural hazards.
For all species, 48.8% of the trees were rated as healthy, 37.3% as slightly defoliated, 13.0%
as moderately defoliated, 0.7% as severely defoliated and 0.2% were dead. The percentage of
damaged trees (defoliation classes 2-4) was 13.9%.
For conifers 15.9% of the trees were classified as damaged (classes 2-4). Picea abies was the
least affected coniferous species with only 12.9% of the trees damaged (defoliation classes 24). For broadleaves 13.4% of the trees were assessed as damaged or dead (classes 2-4).
Among the main broadleaved species, Carpinus betulus had the lowest share of damaged
trees (8.0%), followed by Fagus sylvatica with 9.0%. The most affected species was Robinia
pseudoaccacia with a share of 28.7% damaged or dead trees (classes 2-4). For Quercus sp. a
share of 17.9% trees was rated as damaged or dead.
Compared to 2010, the overall share of damaged trees (classes 2-4) decreased with 3.8 %
points. Forest health status was slightly influenced, mainly for broadleaves, by the relatively
favourable weather conditions during the April - July period.
Concerning the assessment of biotic and abiotic damage factors, most of the observed symptoms were attributed to insects (39.9%), abiotic factors (30.1%), fungi (11.4%), anthropogenic
factors (7.4%), and game and grazing (4.1%).
6.21. Russian Federation
As a total, the condition of 9 116 trees on 295 monitoring plots of Level I established in
Murmansk, Leningrad, Pskov, Novgorod, Kaliningrad oblasts and Karelian Republics was
assessed.
In 2011, compared to 2010, the proportion of not defoliated conifer trees decreased from 84%
to 72% correspondingly. Total number of trees of defoliation classes 1- 4 increased in 2011.
107
The most significant changes have been found for slightly defoliated and, especially, dead
trees: from 14.5% and 0.2% in 2010 to 17% and 4% in 2011 correspondingly.
As for broadleaves, no significant changes have been found, except for dead trees, the proportion of which increased from 0.2% in 2010 to 1.6% in 2011.
As a total for all species, the proportion of not defoliated trees decreased from 83% in 2010 to
77% in 2011. The most significant increase has been found for dead trees: from 0.2% in 2010
to 3.3% in 2011.
The main reason for dead trees increasing were hurricanes of 2010 (the first storm occurred
July 30, after assessment 2010) in Leningrad region.
6.22. Serbia
In the region of the Republic of Serbia, the established 16 x 16 km grid consists of 103 sampling plots and 27 plots added in 4 x 4 km grid. All together number of plots is 130 (not including the autonomous province (AP) of Kosovo and Metohija). The assessment at Level I
was performed according to the ICP Forests Manual of Methods. Actual monitoring has been
carried out in 2011 on 119 plots since few plots were clear cut. In 2011 the researchers of the
NFC Serbia - Institute of Forestry with collaborators from other institution in Serbia, carried
out visual assessment of defoliation and discolouration and collected other necessary field
data.
Defoliation
The total number of trees assessed on all plots was 2 743 of which 333 were conifers and a
considerably higher number i.e. 2 410 were broadleaves. The distribution of the conifers assessed was as follows: Abies alba (21.0%), Picea abies (42.1%), Pinus nigra (20.1% and
Pinus silvestris (16.8%). The most represented broadleaved species were: Carpinus betulus
(5%), Fagus moesiaca (33.2%), Quercus cerris (21%) , Quercus frainetto (15.2%), Quercus
petraea (7.0 %) and other species (18.6%). For coniferous species the assessment resulted in
the following distribution of damage classes:
Picea abies: 87.2% (not defoliated), 11.4% (slight defoliation) 1.4% (moderate defoliation)
0% (severe defoliation and dead trees),
Pinus nigra: 34.3% (not defoliated), 17.9%, (slight defoliation) 35.8% (moderate defoliation),
12% (severe defoliation) 0% (dead trees),
Pinus sylvestris: 89.3% (not defoliated), 8.9% (slight defoliation), 0% (moderate defoliation),
1.8% (severe defoliation), 0% (dead trees),
Abies alba 92.9% (not defoliated), 4.3% (slight defoliation), 0% (moderate defoliation), 2.8%
(severe defoliation), 0% (dead trees),
The percentage of all conifers classified according to damage classes was as follows: no defoliation 78.1 %, slight defoliation 10.8%, moderate defoliation 7.8% and severe defoliation
3.3%.
The assessment of all broadleaved tree species yielded the following distribution: no defoliation 68.6%, slight defoliation 24.2%, moderate defoliation 6.0%, severe defoliation 0.6% and
0.6% dead trees.
As regards the individual broadleaves the assessment brought following results
Carpinus betulus: 84.4% (not defoliated), 8.3% (slightly defoliated), 4.6% (moderately defoliated) 1.8% (severely defoliated), 0.9% (dead trees),
108
Fagus moesiaca: 85.7% (not defoliated), 12.0% (slightly defoliated), 1.6% (moderately defoliated) 0.3% (severely defoliated), 0.4% (dead trees),
Quercus cerris: 61.3% (not defoliated), 34.4% (slightly defoliated), 4.1% (moderately defoliated) 0.2% (severely defoliated), 0% (dead trees),
Quercus frainetto: 66.2% (not defoliated), 26.7% (slightly defoliated), 6.5% (moderately defoliated) 0.3% (severely defoliated), 0.3% (dead trees),
Quercus petraea: 36.3% (not defoliated), 44.0% (slightly defoliated), 17.9% (moderately defoliated) 0% (severely defoliated), 1.8% (dead trees),
Other broadleaves: 57.1% (not defoliated), 28.4% (slightly defoliated), 11.4% (moderately
defoliated) 1.8% (severely defoliated), 1.3% (dead trees).
Discolouration
Discoloration was assessed in main coniferous and broadleaved species according to 5 damage classes as with defoliation. In contrast to defoliation the percentages of the trees showing
no discoloration were very high: Picea abies (99.3%), Pinus sylvestris (92.9%), Abies alba
(92.8%). An exception is Pinus nigra with only 55.2% trees without any discolouration symptoms. Taking all coniferous species together 88.0% trees were not discoloured at all, followed
by 9.9% showing slight discolouration, 1.5% moderately and 0.6% severely discoloured. As
regards the broadleaves the degree of discoloration resembles that of conifers i.e. most of the
trees did not show discolouration. The percentages of trees showing no discolouration are
Carpinus betulus (81.7%), Fagus moesiaca (98.9%), Quercus cerris (none 96.0%), Quercus
frainetto (91.8%), Quercus petraaea (94.6%). In the remaining broadleaves 90% trees assessed are not discoloured. If all broadleaves are included into calculation the percentage of
trees without discolouration is 94.6%, followed by trees slightly discoloured (4.2%), trees
showing moderate (0.7%) and severe discolouration (0.5%). The above results quantify only
the degree of defoliation and discolouration which are unspecific effects caused by adverse
factors such as climate stress, insect pests or pathogenic fungi. Moreover, the foliage density
reflects an adaptation processes to the natural growth condition on particular site.
6.23. Slovak Republic
The 2011 national crown condition survey was carried out on 108 Level I plots of the 16x 16
km grid net. The assessments covered 4 935 trees, 4 017 of which being assessed as dominant
or co-dominant trees according to Kraft. Of the 4 017 assessed trees, 34.7% were damaged
(defoliation classes 2-4). The respective figures were 46.6% for conifer and 26.4% for broadleaves trees. Compared to the 2010, the share of tree defoliated more than 25% decreased by
3.9%. Mean defoliation for all tree species together was 25.4%, with 29.2% for conifers and
22.7% for broadleaved trees. Results show that crown condition in Slovak Republic is worse
than the European average. It is mainly due to worse condition of coniferous species.
Compared to 2010 survey, worsening (average defoliation) was observed in species Fraxinus
excelsior L. and Picea abies Karst.
Since 1987, the lowest damage has been observed for Fagus sylvatica and Carpinus betulus,
with exception of fructification years. The most severe damage has been observed for Abies
alba, Picea abies and Robinia pseudoacacia.
From the beginning of the forest monitoring in 1987 until 1996 the results show a significant
decrease in defoliation and visible forest damage. Since 1996, the share of damaged trees (25-
109
32%) and average defoliation (22-25%) has been relatively stable. The recorded fluctuation of
defoliation depends mostly on meteorological conditions.
As a part of crown condition survey, damage types were assessed. In 2011, 27.3% of all sampling trees (4,935) had some kind of damage symptoms. The most frequent damage was
caused by logging activities (9.3%) and fungi 7.7%) at tree stems. Other damage causes were
abiotic agents (3.3 %), and epiphytes (2.3 %). Epiphytes had the most important influence on
defoliation. 57% of trees damaged by epiphytes revealed defoliation above 25%.
6.24. Slovenia
In 2011 the Slovenian national forest health inventory was carried out on 44 systematically
arranged sample plots (grid 16 x 16 km). The assessment encompassed 1 046 trees, 396 coniferous and 650 broadleaved trees. The sampling scheme and the assessment method was the
same as in the previous years, with the exception on two sample plots, where after felling no
appropriate new trees were present on the plot which could be included in the assessment.
The mean defoliation of all tree species was estimated to 25.4%. Compared to 2010 survey,
worsening of mean defoliation was observed for 0.7% (mean defoliation in 2010 was 24.7%).
In year 2011 mean defoliation for coniferous trees was 25.2% (in year 2010 was 24.1%) and
for broadleaves 25.5% (year before 24.5%). One of the reasons could be that year 2011 was
the fructification year of beech.
In 2011 the share of trees with more than 25% of unexplained defoliation (damaged trees)
reached 31.4%. In comparison to the results of 2010, when the share of trees with more than
25% of unexplained defoliation was 31.8%, the value decreased for 0.4%.
Especially significant is the change of damaged trees for broadleaves where the share of damaged trees increased from 28.1% in year 2010 to 30.0% in 2011, while the share of damage
coniferous decreased from 37.8% in 2010 to 33.6% in 2011.
In the previous year’s coniferous were more damaged than broadleaves. But in year 2011 the
proportion has changed and broadleaves were more damaged then coniferous.
In general, the mean defoliation of all tree species has slightly increased since 1991. The situation improved in year 2010 and in 2011 the mean defoliation increased again for 0.7%.
However the condition of Slovenian forests can be defined as relatively stable.
6.25. Spain
Results obtained in the 2011 inventory show that the general health condition of trees continue with its recovery process. 88.2% of the surveyed trees were healthy, compared to 85.4%
in the previous year, and even better if compared to the situation in 2009 (82.3%). 10.2% of
the trees were included in defoliation classes 2 and 3, indicating defoliation levels higher than
25%, whereas in 2010 this percentage was 12.2% (15.7% in 2009). The number of damaged
trees decreased slightly whereas the number of dead ones decreased more noticeably, reaching
the minimum level of 1.6%.
This overall improvement is more relevant in conifers, with a percentage of 89.6% of healthy
trees (86.9% last year) than in broadleaves (86.8% this year and 83.9% in 2010). The mortality of trees was due to sanitary cuts, forest management operations and to decline processes
derived from specific water deficits.
Regarding other possible damaging agents, there is a clear decrease in its percentage which is
especially remarkable for abiotic damages as well as for insects and fungi.
110
There are drastic reductions both in spring defoliators on broadleaves and in pine processionary caterpillar (Thaumetopoea pityiocampa).
In general lines, also conifer borers show this decrease, associated with the least number of
trees damaged by abiotic agents (mainly drought).
The unchanged and a slight and punctual localized increase in the numbers of some broadleaves borer and some specific defoliator should be quoted as well. The same trend is observed in the records for fungi, in particular for those which are affecting foliage of broadleaves and the vascular ones
However there is a certain increase in fungi affecting needles of conifers, particularly Sirococcus.
Mistletoe damages and degenerative processes affecting juniper stands remain broadly stable,
if compared to the previous year. The specific damages which were observed in previous
years on alders seem not to have followed an upward trend, and there has not been an increase
in damages related to Seca syndrome.
The importance of atmospheric pollution in the evolution of forest condition is a factor which
cannot be quantified directly, as it is frequently disguised by other kind of processes which
are more apparent. However, in combination with other agents it can contribute to the degradation processes of forests.
6.26. Sweden
The national results are based on assessment of the main tree species Picea abies and Pinus
sylvestris in the National Forest Inventory (NFI), and concerns, as previously, only forest of
thinning age or older. In total, 7 596 trees on 3 223 sample plots were assessed. The Swedish
NFI is carried out on permanent as well as on temporary sample plots. The permanent sample
plots, which are two thirds of the total sample, are re-measured every 5th year.
The proportion of trees with more than 25% defoliation is for Norway spruce (Picea abies)
29.6% (28.9% in 2010) and for Scots pine (Pinus sylvestris) 10.0% (11.7 % in 2010). Greater
defoliation is seen in southern Sweden on Scots pine while an improvement is apparent in
central Sweden. However, the majority of changes seen in defoliation levels during recent
years are minor.
An outbreak of bark beetles (Ips typographus and Polygraphus poligraphus) has been seen in
central Sweden since 2008. In one county (300 000 ha older spruce forest) a special target
inventory of spruce trees killed by bark beetle was undertaken. The results from the special
target inventory in 2011 show that the quantity Norway spruce trees killed was about the
same for the two bark beetle species and in total effected a standing volume of 850 000 m3sk.
It seems that in the two previous year’s even larger volumes were affected.
Needle loss on Scots pine caused by European pine sawfly (Neodiprion sertifer) is still seen
in southeastern Sweden. Damage on Norway spruce in southernmost Sweden by spruce scale
(Physokermes inopinatus) has ceased as the insect population collapsed. The decline in Fraxinus excelsior is continuing in southern Sweden. In northern Sweden resin top disease
(Cronartium flaccidum) is still a problem in young Scots pine stands. Damage on forest plantation by rodents has increased, especially in northern Sweden, but a decrease in the population of rodents is foreseen in 2012. Still, the most important damage problems are due to pine
weevil (Hylobius abietis) (in young forest plantations), browsing by ungulates, mainly elk, (in
young forest), and root rot caused by Heterobasidion annosum. The invasive species Lep-
111
toglossus occidentalis was in 2011 found for the first time in Sweden. The species has its origin in western North America and lives on the seeds of conifers.
6.27. Switzerland
In 2011 the Swiss national forest health inventory was carried out on 47 plots of the 16 x 16
km Level I grid using the same sampling and assessment methods as in the previous years.
Crown condition of most tree species in 2011 decreased substantially as compared to 2010. In
2011 30.8% of the trees had more than 25% unexplained defoliation (i.e. subtracting the
known causes such as insect damage or frost damage) as compared to 22.2% in 2010. In 2011
41.3% of the trees had more than 25% total defoliation (2010: 32.0%). The same increase was
observed for most species for the Level II plots. Only oak species showed lower crown defoliation in 2011.
The very high defoliation in 2011 can be explained by two main factors: the extremely high
seed production of almost all main tree species and the very dry first half of the year in Northern and Western Switzerland and the inner-alpine valleys.
For common beech, Norway spruce, oaks, sycamore maple record high proportions of fruit
baring trees were recorded and silver fir and European larch were close to maxima. Beech
trees with recorded high levels of seed increased in crown defoliation compared to 2010 by
10%, trees with low seed production by 9%, while trees without observable seeds decreased
in defoliation by 2%. In 2009, the second highest seed year, only for trees with high seed
amounts an increase in defoliation had been observed. For Norway spruce and silver fir on
Level I plots in 2011, the trees with cone production also showed a higher increase in defoliation than trees without or with few cones. However, the relationship was less clear than for
beech and varied by plots. During branch sampling for foliar chemical analysis on selected
Swiss Level II sites, branches of some Norway spruce trees were found with a large amount
of male and female flowers, but almost no new shoot and needles formed in 2011. The replacement of normal shoots by male flowers may partially explain the increased crown transparency in 2011. For oak, in 2011 the proportion of recorded insect decreased resulting in a
decrease in crown defoliation despite the high seed production. This may be due to the extremely early leaf unfolding of the oaks in 2011 before some defoliators emerged.
On some Swiss Level II sites the soil water potential measured by the end of June (the beginning of the crown defoliation survey) was the lowest recorded since the beginning of measurements in 1997. This was due to the record low precipitation in parts of Switzerland during
the first half of 2011 (mainly April and May). Only the southern alpine sites and the sites at
higher altitude in the Alps showed little or no water shortage.
Annual mortality rates in the Level I survey were higher than usual, but could be explained by
the death of several chestnut coppice trees on some plots. On one Level II plot Scots pine
mortality increased to 4% as a result of the drought in 2011. The crown defoliation and the
proportion of common ash trees with high defoliation increased for the first time since the
observation of the ash wilting. This was also true on sites, where other species showed no
change in crown defoliation.
112
6.28. Turkey
The national results for crown conditions were obtained from 13 282 trees in 563 Level I plots
located in 16 km x 16 km grid. About 63.9% of the assessed trees were conifers, 36.1% were
broadleaves. The most common species were Pinus brutia, Pinus nigra, Quercus cerris and
Fagus orientalis and their account for 24.2%, 18.5 %, 7.3% and 6.9% of the assessed trees,
respectively. The assessment includes 61 different tree species and most of them are broadleaves.
The mean defoliation rates 19.3% for broadleaves. The defoliation rates for the assessed main
broadleaves is descending in the following order; Quercus pubescens (29.6%), Quercus petraea (25.1%) and Castanea sativa (23.7%). About 31.5% of the broadleaves have no defoliation (class 0). 17.3% of the observed trees had defoliation rates greater than 25% (classes 2, 3
and 4).
The mean defoliation rates greater than 35% for the other less common tree species such as
Prunus avium, Populus nigra, Salix alba and Ulmus glabra.
The mean defoliation rates were 17.1% for the conifers. Juniperus communis, with 21.0%, has
the highest defoliation rate among the conifers. Pinus pinaster and Pinus brutia followed Juniperus communis with 20.4% and 19.7% mean defoliation rates, respectively. About 33.9%
of the conifers have no defoliation (class 0). However, 11.6% of the observed all conifer trees
had defoliation rates greater than 25% (classes 2, 3 and 4).
The assessed over all tree species, the mean defoliation rates were 17.9%. The healthy trees
(class 0) rates were about 33%. But, 13.6% of the observed trees are having more than 25%
defoliation rate (classes 2, 3 and 4).
Insects account for about 32.2% of the damaging agents. Thaumetopoea pityocampa and
Lymantria dispar are the most common species observed in the Level I plots. Viscum alba, a
parasitic plant species, is considered responsible for the 8.5% of the defoliation.
Similar to the previous years, defoliation rates of broadleaves are higher than that of the conifers. Estimated defoliation rates are higher for the tree species in the northern and northeastern
part of the country. However, there has been an improvement in health status of trees for the
last four years.
6.29. Ukraine
In 2011 the organization of forest monitoring field works was changed due to order of State
Agency of Forest Resources of Ukraine No 60 from 30 March 2011. Starting from the field
season 2011 the responsibility for collecting the data has been entrusted to the State Forest
Management Enterprises (SFME’s). Assessment of indicators for monitoring sites level 1 is
carried out by specialists of SFME’s under the methodological guidance experts from Ukrainian Research Institute of Forestry and Forest Melioration (URIFFM) and officers from Regional Forest Administrations (RFA). Responsibility for QA/QC of forest monitoring data is
placed to RFA and URIFFM, experts from URIFFM is responsible for maintaining of national
forest monitoring data base. The series of trainings for officers from RFA and for field specialists from SFME’s has been conducted in 2011 for standardization in assessment of defoliation and others indicators.
In 2011, 3 3878 sample trees were assessed on 1 476 forest monitoring plots in 25 administrative regions of Ukraine. Mean defoliation of conifers was 11.3% and of broadleaved trees was
11.7%.
113
For the total sample some deterioration of tree condition was observed compared to the previous year. In 2011, the percentage of healthy trees slightly decreased (64.9% against 67.7%).
At the same time, the share of the slight to moderate defoliated trees increased from 32% to
35%. These changes may be related to change of sample volume.
For the sample of common sample trees (CSTs) (32,160 trees) the tendency to deterioration of
crown condition was observed. Mean defoliation slightly increased in 2011 (11.5%) comparing to 2010 (10.9%).
Some deterioration of tree condition was registered for CSTs of Picea abies, Pinus sylvestris
and Fraxinus excelsior, that is characterized by statistically significant decreasing of share of
trees in defoliation class 0 (for Picea abies on 4.1%, for Pinus sylvestris – on 3% and Fraxinus excelsior – on 3.1%) and increasing in all other classes. Changes in distribution within
defoliation classes of CSTs of other main tree species (Fagus sylvatica and Quearcus robur)
were insignificant.
Some deterioration of tree condition may be explained post-pointed effect of extreme hot and
dry weather condition in summer 2010 that may cause some weakening of trees and increasing of defoliating insect’s impact.
6.30. United States of America
Within the USDA Forest Service critical loads for acidification and nutrient nitrogen are being determined through collaboration between research and management branches. This information was recently used to support watershed condition assessments in the US. The Forest Service is also working cooperatively with other federal agencies (i.e., U.S. National Park
Service, U.S. Environmental Protection Agency, U.S. Geological Survey) to develop a national database of critical loads that can be used to identify at risk ecosystems to air pollution,
assess air pollution impacts, and track the effectiveness of air pollution control strategies.
This interagency critical loads group (CLAD, Critical Loads of Atmospheric Deposition Science Committee of the National Atmospheric Deposition Program) is also actively working
with international organizations (i.e. North American Forest Commission, Canada Transboundary Pact, United Nations Economic Commission for Europe (UNECE) ICP Forests)
who have been using critical loads in air quality management and policy development for
over two decades.
Phase I of this project was completed in 2011. The Western Governors’ Association (WGA)
provided Phase I project coordination with oversight from the U.S. National Park Service, and
input from the USDA Forest Service, and Environmental Protection Agency. The WGA provided active leadership in project coordination and management of the Phase I pilot study
effort to provide comprehensive, regionally-scaled U.S. critical loads data to the UNECE Coordination Centre for Effects (CCE) by the CCE-driven due dates. Iterative collaboration to
gather and collate data and Critical Loads results with scientists and researchers from the U.S.
NPS, the USDA FS, and the U.S. EPA, along with private consultants and university faculty
was employed to create shared data and map products, in order to deliver the Phase I project’s
U.S. Critical Loads data, maps, and analytical results. For the first time, these U.S. results
were distributed to the CCE by the mid-March 2011 CCE deadline and then presented at the
April 18-21 CCE workshop. The U.S. NPS, USDA FS, and U.S. EPA designed the FOCUS
Phase I project to coordinate input from members of the Critical Loads’ research community,
in order to summarize, compile in a database, and document research results and data for publication in maps and other products.
114
In 2011 the USDA Forest Service published a monograph on empirical critical loads for nitrogen in the major ecoregions of the United States. This is the first time that a synthesis of
critical loads for the entire United States has been published. A synthesis review article based
on this work was also published in the journal Ecological Applications. These publications
review the empirical critical loads for ecoregions ranging from tundra and taiga to various
forest types, deserts, Mediterranean California, deserts, and tropical and subtropical forests,
wetlands and inland surface waters. Comparisons are made to European critical loads and
factors affecting the critical load are discussed. Critical load maps and exceedance maps are
also included in these reports.
Map showing the CL exceedance of N deposition for herbaceous plants and shrubs by ecoregion in the United States. The exceedance indicates when regions are at risk for detrimental
effects from N deposition. Reliability of estimates is shown with hatching.
A workshop with participation of the ICP Forests, Canadian and Mexican experts was held in
Riverside, CA in April 2011 to discuss the USDA Forest Service capacity to integrate measurements and modelling efforts related to the computation of critical loads/levels and their
exceedances and to further develop collaboration with the ICP Forests. One of the workshop
conclusions was that the USDA Forest Service Forest Inventory and Analysis (FIA) forest
health monitoring plots (P3-similar to ICP Level II plots) should include the Experimental
Forest & Rangeland network sites where data needed for evaluation of critical loads (such as
wet deposition, or air chemistry) are collected.
Annex I: Maps of the transnational evaluations
Annex I-1: Broadleaves and conifers
Shares of broadleaves and conifers assessed on Level I plots in 2011
115
116
Annex I-2: Percentage of trees damaged (2011)
Percentage of trees assessed as damaged (defoliation >25) on Level I plots in 2011
Annex I-3: Mean plot defoliation of all species (2011)
Mean plot defoliation of all species (2011)
117
118
Annex I-4: Changes in mean plot defoliation (2010-2011)
Changes in mean defoliation of all trees assessed on Level I between 2010 and 2011
119
Annex II: National results
Annex II-1: Forests and surveys in European countries (2011)
Total
area
(1000 ha)
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
2875
46.8
8385
20760
3035.1
11000
5654
925.1
7886
4310
4510
Forest
area
(1000 ha)
1063
17.7
3878
8010
700.4
3820
2061
297.7
2647
586
2212
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of Macedonia
Rep. of Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Fed.
30415
54883
35702
12890
9300
7028
30128
6459
16
6530
259
20150
15840
11076
2034
1922
680
8675
3162
8
2170
89
17974
4041
6490
954
220
399
1735
1452
6
1158
30
1897
9884
3857
1080
1702
37
6940
1710
2
900
54
3384
3482
32376
31268
8893
23839
1700075
360
12000
9200
3234
6233
809090
5.36
140
6800
6955
1081
1873
405809
287.19
136
5200
2245
12000
9200
4360
195769
36173
Serbia
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom
TOTAL
8836
4901
2014
50471
41000
4129
77846
60350
20933
2336794
2360
1961
1183
18173
28300
1279
21537
9400
2665
1018073.8
179
815
445
6600
19600
778
13158
2756
1306
515644.16
2181
1069
738
9626
900
501
8379
3285
854
274675.19
Participating
Countries
Coniferous
forest
(1000 ha)
171
15.4
2683
4785
281
1040
321
172
2014
289
1113.4
Broadleaves
forest
(1000 ha)
600
2.3
798
3225
324
2398
1740
0
633
263
1098.7
Area
surveyed
(1000 ha)
19871
13100
10347
17.7
8010
138
2647
2212
1922
1868
1961
1183
20600
1279
9057
6033
157618.7
Grid
No. of
size
sample
(km x km)
plots
no survey in 2011
16 x 16
3
no survey in 2011
16 x 16
416
4² / 8²
112
4²/8²/16²
159
16 x 16
92
16x16
15
8²/16²
136
7²/16²
18
16 x 16
98
16² /
24x32
717
554
16² / 4²
410
no survey in 2011
16 x 16
78
no survey in 2011
253
8x8
no survey in 2011
8x8/16x16
1009
no survey in 2011
no survey in 2011
2x2
567
no survey in 2011
3²/9²
1735
16 x 16
1947
no survey in 2011
16 x 16
242
32x32
295
16 x 16/4
x4
130
16 x 16
108
16 x 16
44
16 x 16
620
varying
3223
16 x 16
47
16 x 16
563
16 x 16
1476
no survey in 2011
varying
15067
No. of
sample
trees
72
9583
2594
5583
2208
360
5418
411
2372
4147
11352
10167
1830
8099
5738
12552
9417
38940
5808
9116
2743
4017
1046
14880
7596
1008
13282
33878
224219
120
Annex II-2: Percent of trees of all species by defoliation classes and class aggregates
(2011)
Participating
Area
No. of
0
1
2
3+4
countries
surveyed
sample
none
slight
moderate
severe
(1000 ha)
trees
18
72
63.9
27.8
8010
2212
19871
13100
10347
9583
2594
5583
2208
362
5418
411
2372
4147
11352
10167
30.5
22.8
28.2
40.5
12.5
15.2
65.2
50.8
53.7
26.0
36.7
63.4
53.7
50.2
34.2
71.1
32.1
24.8
41.1
35.7
34.1
35.3
1922
1830
62.3
18.8
8099
6644
27.0
13.8
41.7
72.2
5738
15.6
69.0
12552
45.0
36.6
9852
38940
40.9
14.0
38.2
62.1
5808
9116
2743
4017
1046
14880
7596
1008
13282
33878
48.8
77.2
69.8
9.2
18.0
28.1
50.5
21.1
33.0
64.9
37.3
14.5
22.6
56.1
50.7
60.1
30.6
48.0
53.4
28.3
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of Macedonia
Rep. of Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Fed.
Serbia
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom
138
2647
12000
9200
36173
1868
1961
1183
20600
1279
9057
6033
and dead
no survey in 2011
8.3
0.0
no survey in 2011
4.8
1.3
21.4
2.1
17.9
3.7
21.4
3.9
15.8
0.6
50.9
1.8
7.1
2.9
5.7
2.4
9.3
1.3
35.4
4.5
26.1
1.9
no survey in 2011
13.8
5.1
no survey in 2011
27.4
3.9
12.4
1.6
no survey in 2011
13.2
2.2
no survey in 2011
no survey in 2011
15.7
2.7
no survey in 2011
17.4
3.5
22.9
1.1
no survey in 2011
13.0
0.9
4.3
4.0
6.2
1.4
33.0
1.7
26.7
4.7
9.1
2.7
12.8
6.1
20.4
10.5
11.4
2.3
6.3
0.5
no survey in 2011
2+3+4
8.3
6.1
23.5
21.6
25.2
16.4
52.7
10.0
8.1
10.6
39.9
28.0
18.9
31.3
14.0
15.4
18.4
20.9
24.0
13.9
8.3
7.6
34.7
31.4
11.8
18.9
30.9
13.6
6.8
Andorra, Cyprus, Ireland, Sweden: only conifers assessed.
Note that some differences in the level of damage across national borders may be at least partly due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time.
121
Annex II-3: Percent of conifers by defoliation classes and class aggregates (2011)
Participating
countries
Coniferous
forest
(1000 ha)
No. of
sample
trees
0
none
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of
Macedonia
Rep. of
Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Fed.
Serbia
Slovak
Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United
Kingdom
1
slight
2
moderate
3+4
severe
and dead
2+3+4
no survey in 2011
18
72
63.9
4785
172
6821
750
2397
350
360
27.9
17.8
19.1
26.0
12.5
27.8
8.3
no survey in 2011
66.3
4.8
67.0
14.4
47.6
28.6
28.9
35.1
71.1
15.8
2014
4201
14.0
27.1
289
1113
17974
4041
6490
229
2071
4147
3925
6083
77.7
47.6
51.6
40.2
43.2
220
244
43.0
1452
1857
4757
33.7
8.0
1158
3431
14.5
16.6
4.4
43.7
6.3
36.7
10.4
27.9
28.8
36.5
18.9
no survey in 2011
28.3
21.3
no survey in 2011
38.5
25.1
76.0
14.4
no survey in 2011
69.2
15.1
no survey in 2011
1040
56.7
0.0
8.3
1.0
0.9
4.8
10.0
0.6
5.8
15.2
33.3
45.1
16.4
2.2
58.9
1.3
2.4
1.3
3.1
1.4
5.7
8.7
11.7
31.9
20.3
7.4
28.7
2.7
1.6
27.8
16.0
1.2
16.3
0.0
32.0
2.9
1.0
17.3
24.2
1.3
5.2
3.3
15.9
10.6
11.1
no survey in 2011
5
75
26.9
41.1
32.0
no survey in 2011
35.6
14.4
64.5
23.3
no survey in 2011
31.3
14.6
17.0
5.4
10.8
7.8
6800
6955
7499
25683
47.1
11.3
1873
405809
179
1104
5713
333
52.8
72.4
78.1
815
1657
4.3
49.1
43.2
3.4
46.6
445
396
7439
7596
722
8488
13742
22.0
32.5
50.5
19.3
33.9
66.9
44.4
57.1
30.6
49.2
54.5
26.3
28.3
8.0
12.8
22.5
10.3
6.5
5.3
2.4
6.1
9.0
1.3
0.3
33.6
10.4
18.9
31.5
11.6
6.8
19600
778
13158
2756
no survey in 2011
Note that some differences in the level of damage across national borders may be at least partly due to differences
in standards used. This restriction, however, does not affect the reliability of the trends over time.
122
Annex II-4: Percent of broadleaves by defoliation classes
and class aggregates (2011)
Participating
countries
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of Macedonia
Rep. of Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
Serbia
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom
Broadleav.
forest
No. of
sample
(1000 ha)
trees
0
None
1
slight
2
3225
2762
1844
3186
1858
37.0
24.9
35.1
43.3
633
263
1099
1897
9884
3857
1217
182
301
4147
7407
4084
19.3
58.4
73.1
62.2
18.5
27.4
1702
1586
65.3
1710
5838
1887
24.4
28.5
900
2307
17.3
2398
2
moderate
no survey in 2011
only conifers assessed
no survey in 2011
56.6
4.7
48.2
24.0
52.1
9.9
35.3
18.8
49.5
30.7
28.8
10.2
23.9
1.3
31.7
5.1
37.3
38.9
34.6
35.9
no survey in 2011
17.4
12.6
no survey in 2011
42.9
28.2
62.7
7.2
no survey in 2011
68.9
10.4
no survey in 2011
3+4
severe
and
dead
2+3+4
1.7
2.8
2.9
2.7
6.4
26.7
12.8
21.5
0.5
2.6
1.7
0.9
5.3
2.1
31.2
12.8
3.0
6.0
44.2
38.0
4.7
17.3
4.5
1.6
32.7
8.8
3.4
13.8
2.8
18.4
5.4
1.4
32.3
23.5
0.7
13.4
1.8
4.3
1.2
0.5
4.3
3.0
7.2
26.4
30.0
13.2
13.9
4.0
0.6
29.6
17.2
6.7
no survey in 2011
287
12444
45.0
5200
2245
2353
13257
21.0
19.1
4360
4704
47.8
195770
3403
85.3
2181
1069
738
2410
2360
650
7441
68.6
12.7
15.5
23.7
501
8379
3285
284
4794
20136
24.9
31.5
63.6
36.6
15.6
no survey in 2011
46.8
26.9
57.4
22.1
no survey in 2011
38.8
12.7
10.4
2.5
24.2
6.0
60.9
25.9
54.5
25.7
63.1
10.3
45.5
15.7
51.3
13.3
29.6
6.1
no survey in 2011
Andorra, Cyprus, Ireland, Sweden: only conifers assessed.
123
Annex II-5: Percent of damaged trees of all species (2000-2011)
Participating
Countries
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France ·
Germany a)
Greece
Hungary
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of
Macedonia
Rep. of
Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Fed.
Serbia
Slovak
Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United
Kingdom *
All species
Defoliation classes 2-4
2000
2001
2002
2003
2004
2005
2006
10.1
10.2
13.1
11.1
11.3
17.3
33.7
22.0
18.4
12.2
36.1
13.1
10.0
19.4
39.7
25.2
12.2
14.8
9.0
19.9
35.0
27.1
10.8
11.1
23.0
15.0
7.9
17.9
37.4
24.9
20.8
8.9
24.0
19.0
46.3
23.4
9.7
20.7
17.9
33.8
25.0
8.9
10.2
9.5
17.8
37.1
20.6
2.8
51.7
52.1
53.4
54.4
57.3
57.1
11.0
7.4
11.6
18.3
23.0
18.2
20.8
14.6
34.4
20.7
7.4
8.5
11.0
20.3
21.9
21.7
21.2
17.4
38.4
15.6
8.7
7.6
11.5
21.9
21.4
20.9
21.2
20.7
37.3
13.8
10.2
7.6
10.7
28.4
22.5
11.8
5.3
9.8
31.7
31.4
22.5
13.9
37.6
12.5
21.5
17.4
35.9
12.5
9.4
5.4
8.8
34.2
28.5
16.3
21.0
16.2
32.9
13.1
13.9
23.4
11.7
12.8
14.7
13.9
11.0
Change
% points
2010/2011
2007
2008
2009
2010
2011
47.2
15.3
6.8
8.3
-7.0
8.1
16.4
29.7
25.1
16.7
8.0
14.5
31.9
23.9
47.0
8.4
20.2
21.1
26.3
36.2
15.3
14.2
7.4
22.1
23.8
27.9
19.2
6.1
23.5
21.6
25.2
16.4
-1.3
1.4
-2.2
-2.7
-2.8
56.2
7.6
6.2
9.7
35.6
27.9
57.1
6.1
6.8
10.5
35.4
24.8
56.7
9.1
9.0
10.2
32.4
25.7
-1.5
0.7
0.0
0.1
5.3
4.8
20.7
6.0
35.7
15.0
18.9
-2.9
10.0
32.8
15.3
54.2
9.3
8.1
10.5
34.6
23.2
23.8
21.8
17.5
29.8
13.4
52.7
10.0
8.1
10.6
39.9
28.0
19.2
7.4
30.5
13.4
56.8
5.5
7.2
9.1
33.5
26.5
24.3
18.4
12.5
35.8
13.8
31.3
14.0
1.5
0.6
12.0
12.3
19.6
17.7
21.3
15.4
-5.9
23.0
29.1
21.8
24.3
32.0
10.3
14.3
36.9
42.5
42.4
34.0
26.5
27.6
32.5
33.6
25.2
22.5
18.4
-4.1
22.7
18.0
21.0
17.7
18.9
20.7
20.9
24.0
2.0
3.3
17.8
4.4
10.8
13.9
8.3
7.6
-3.9
3.9
-3.2
38.6
31.8
14.6
19.2
22.2
16.8
5.8
34.7
31.4
11.8
18.9
30.9
13.6
6.8
-3.9
-0.4
-2.8
-0.3
8.7
-3.2
1.0
21.7
25.5
32.7
9.6
13.5
10.9
3.9
18.0
22.9
34.7
13.0
12.6
27.5
20.7
34.6
16.6
11.7
30.2
21.6
30.7
24.3
8.1
19.5
23.3
20.1
26.2
20.2
8.6
23.2
8.4
19.9
27.2
30.6
10.1
13.3
9.8
14.0
22.8
14.3
16.4
11.3
15.4
11.5
18.9
6.2
10.3
23.5
31.7
24.8
31.4
26.7
22.9
24.8
13.8
13.7
29.4
28.9
13.0
17.5
18.2
28.1
16.4
16.8
18.6
27.5
16.6
19.2
14.9
29.3
15.0
16.5
29.1
30.6
21.3
18.4
28.1
28.1
29.4
21.5
19.4
22.6
25.6
35.8
17.6
17.9
22.4
60.7
39.6
27.7
27.0
29.9
8.7
6.6
7.1
29.3
36.9
15.6
17.3
19.0
24.6
8.2
32.1
35.5
17.7
15.1
18.3
18.7
6.8
25.9
26.0
21.6
21.1
27.3
24.7
26.5
24.8
48.5
124
Annex II-6: Percent of damaged conifers (2000-2011).
Participating
countries
Conifers
2004
2005
2006 2007
14.0
13.6
36.1
47.2
23.0
13.1
15.1
14.5
8.9
8.4
8.1
7.5
15.6
16.8
13.9
15.8
47.1
45.4
37.4
47.6
70.6
79.5
61.1
71.7
12.2
10.8
16.7
20.8
62.6
62.7
62.9
62.3
5.8
5.5
3.1
1.7
5.3
5.6
6.7
6.0
10.1
9.2
10.4
9.6
18.6
20.8
24.1
23.6
26.3
24.9
20.2
22.7
15.0
24.2
22.0
22.3
20.8
17.4
16.2
6.2
7.4
21.7
22.8
22.7
19.5
11.9
13.2
16.2
15.2
change
% points
2010/
2011
2000 2001 2002 2003
2008
2009 2010 2011
Albania
12.3
12.4
15.5
Andorra
15.3
6.8
15.3
8.3
-7.0
Austria
9.1
9.6
10.1
11.2
14.5
Belarus
26.1
23.4
9.7
9.5
8.1
8.3
7.7
5.8
-1.9
Belgium
19.5
17.5
19.7
18.6
13.2
13.6
16.2 15.2
-1.0
Bulgaria
46.4
39.1
44.0
38.4
45.6
33.0
31.1 33.3
2.2
Croatia
53.3
65.1
63.5
77.4
59.1
66.5
56.9 45.1
-11.8
Cyprus
8.9
2.8
18.4
46.9
36.2
19.2 16.4
-2.8
Czech Republic
58.3
58.1
60.1
60.7
62.8
63.1
60.1 58.9
-1.2
Denmark
8.8
6.7
4.5
6.1
9.9
1.0
5.4
5.7
0.3
Estonia
7.5
8.8
7.9
7.7
9.3
7.5
9.0
8.7
-0.3
Finland
12.0
11.4
11.9
11.1
10.1
9.9
10.6 11.7
1.1
France
12.0
14.0
15.2
18.9
25.1
26.8
27.4 31.9
4.5
Germany a)
19.6
20.0
19.8
20.1
24.1
20.3
19.2 20.3
1.1
Greece
16.5
17.2
16.1
26.3
23.7
Hungary
21.5
19.5
22.8
27.6
27.1
35.1 28.7
-6.4
Ireland
14.6
17.4
20.7
13.9
10.0
12.5
17.5
Italy
19.2
19.1
20.5
20.4
24.0
31.6
29.1 32.2
3.1
Latvia
20.1
15.8
14.3
12.2
16.7
14.8
15.0 16.0
1.0
Liechtenstein
Lithuania
12.0
9.8
9.3
10.7
10.2
9.3
10.2
19.1
17.4
19.8 16.3
9.5
-3.5
Luxembourg
7.0
Rep. of Macedonia
Rep. of
55.4
35.5
38.0
34.3
33.3 32.1
Moldova
38.6
-1.2
Netherlands
23.5
20.7
17.5
9.4
17.2
17.9
15.3
Norway
21.8
25.1
24.1
21.2
16.7
19.7
23.0
19.2
17.9
16.4 17.3
20.2
0.9
Poland
32.1
30.3
32.5
33.2
33.4
29.6
20.9
17.5
17.2
20.3 24.2
21.1
3.9
Portugal
4.3
4.3
3.6
5.3
10.8
17.1
Romania
9.8
9.6
9.9
9.8
7.6
4.7
21.8
21.7
16.1 15.9
5.2
-0.2
Russian Fed. c)
9.8
10.0
7.3
5.1
10.6
5.5
Serbia
10.0
21.3
7.3
39.6
19.8
21.3
13.3
13.0
12.6
12.0 11.1
12.6
-0.9
Slovak Republic 37.9
38.7
40.4
39.7
36.2
35.3
37.5
41.1
42.7
46.8 46.6
42.4
-0.2
Slovenia
34.5
32.2
31.4
35.3
37.4
33.8
36.0
40.7
38.8
37.8 33.6
32.1
-4.2
Spain
12.0
11.6
15.6
14.1
14.0
19.4
15.8
12.9
14.9
13.1 10.4
18.7
-2.7
Sweden
13.5
18.4
17.7
20.4
16.0
19.6
17.9
17.3
15.1
19.2 18.9
20.1
-0.3
Switzerland
33.0
19.1
19.9
13.3
27.4
28.2
20.7
18.7
18.8
20.9
31.5
22.5
10.6
Turkey
8.1
16.2
16.0
14.5 11.6
-2.9
Ukraine
47.3
16.8
14.6
15.4
11.4
8.1
7.1
7.1
6.3
5.6
6.8
6.9
1.2
United King20.2
20.6
25.1
25.8
23.2
22.2
23.3
16.1
38.6
dom*
Andorra: observe the small sample size. Austria: From 2003 on results are based on the 16 x 16 km transnational grid net
and must not be compared with previous years. Poland: Change of grid net since 2006. Russian Federation: North-western
and Central European parts only. Ukraine: Change of grid net in 2005. Hungary, Romania: Comparisons not possible due to
changing survey designs. Note that some differences in the level of damage across national borders may be at least partly
due to differences in standards used. This restriction, however, does not affect the reliability of the trends over time.
125
Annex II-7: Percent of damaged broadleaves (2000-2011).
2000
8.4
2001
8.4
2002
10.7
2003
2004
10.3
7.6
16.9
18.8
45.8
18.3
10.4
13.3
18.3
26.0
18.7
11.3
9.0
17.0
29.0
14.4
10.2
15.8
16.6
27.2
14.3
13.6
12.9
21.3
30.1
17.2
Czech Republic
21.4
21.7
19.9
24.4
Denmark
Albania
Andorra
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Change
% points
2010/201
1
Broadleaves
Participating
countries
2005
2006 2007 2008
8.5
12.9 20.1
10.6
8.2
7.6
8.9
21.4 18.8 17.5 15.3
23.1 36.4 21.1 17.8
19.2 18.2 20.0 19.1
2009
2010
2011
8.7
23.4
12.2
20.7
10.5
6.9
24.6
18.2
21.9
6.4
26.7
12.8
21.5
-0.5
2.1
-5.5
-0.4
31.8
32.0
31.2
33.5
32.2
32.9
32.2
31.2
-1.0
0.7
13.9
8.5
15.4
16.6
19.1
14.4
14.8
10.3
8.0
10.0
12.1
12.8
Estonia
9.5
2.1
2.7
6.7
5.3
3.4
8.6
7.6
3.4
3.5
2.5
3.0
0.5
Finland
9.9
8.8
8.8
8.3
8.4
7.2
10.3
10.9
10.6
4.7
9.2
6.0
-3.2
France ·
21.6
23.6
25.5
33.5
38.7
41.3
42.0
41.6
36.5
37.1
38.7
44.3
5.6
Germany a)
29.9
25.4
24.7
27.3
41.5
35.8
37.2
32.8
28.4
36.1
29.4
38.0
8.6
Greece
20.2
26.6
26.5
Hungary
20.8
21.5
20.8
22.0
21.0
20.9
19.0
20.6
-2.4
Italy
40.5
46.3
44.6
45.0
42.0
36.5
35.2
40.4
Latvia
Liechtenstein
Lithuania
Luxembourg
Rep. of
Moldova
Netherlands
Norway
Poland
Portugal
Romania
Russian Fed. c)
Serbia b)
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom *
22.2
14.8
12.8
13.5
14.3
12.9
8.5
11.8
17.7
33.5
16.3
19.0
24.6
21.8
15.4
16.6
29.2
36.9
42.5
42.3
33.9
26.4
18.8
34.0
32.0
13.2
15.8
18.5
33.7
31.4
12.8
14.7
33.7
29.0
39.6
16.2
13.3
46.9
33.2
38.7
19.0
13.0
53.1
27.6
34.1
27.0
9.3
6.7
13.9
18.4
15.7
7.5
22.1
6.7
26.9
26.7
14.4
14.1
16.3
29.6
30.4
33.1
12.6
14.8
16.0
0.6
14.5
25.9
17.3
9.6
16.0
21.5
25.6
22.6
19.1
11.1
18.1
13.5
19.9
24.2
16.1
8.3
32.8
69.6
53.3
36.7
35.3
23.8
21.9
30.3
23.2
17.9
5.2
23.9
17.1
19.7
17.3
35.8
36.8
30.1
32.7
2.6
11.5
11.6
9.4
8.8
-0.6
17.7
20.3
18.4
23.7
13.8
-9.9
32.5
33.6
25.2
22.4
18.4
-4.0
36.3
18.9
33.8
19.1
31.0
18.5
26.8
21.5
32.3
23.5
5.5
2.0
18.0
3.2
10.7
32.9
28.1
16.1
13.4
4.3
7.2
26.4
30.0
13.2
-4.6
1.1
-3.5
-6.5
1.9
-2.9
25.2
21.2
6.4
29.6
17.2
6.7
4.4
-4.0
0.3
Ireland
27.6
26.2
33.2
18.0
9.9
23.5
15.7
13.6
28.5
23.3
9.2
27.9
11.0
17.0
27.6
24.4
10.8
22.6
15.7
16.6
35.7
19.5
11.3
20.8
34.6
18.4
18.3
4.4
9.9
24.5
33.3
20.7
26.1
43.2
9.2
6.2
7.1
19.6
38.3
9.1
17.4
23.4
7.2
30.6
28.2
29.2
35.3
56.1
126
Annex II-8: Changes in defoliation (1989-2011)
Albania
Andorra
Austria
127
Belarus
Belgium
Bulgaria
128
Croatia
Cyprus
Czech Republic
129
Denmark
Estonia
Finland
130
France
Germany
Greece
131
Hungary
Ireland
Italy
132
Latvia
Liechtenstein
Lithuania
133
Luxembourg
FYR of Macedonia
Republic of Moldova
134
Netherlands
Norway
Poland
135
Portugal
Romania
Russian Federation
136
Serbia
Slovak Republic
Slovenia
137
Spain
Sweden
Switzerland
138
Turkey
Ukraine
United Kingdom
Annex III: Addresses
Annex III-1: UNECE and ICP Forests
UNECE
United Nations Economic Commission for Europe
LRTAP Convention Secretariat
Palais des Nations
1211 GENEVA 10
SWITZERLAND
Phone: +41 (22) 917 23 58/Fax: +41 (22) 917 06 21
email: [email protected]
Mr Krzysztof Oledrzynski
ICP Forests
International Co-operative Programme on Assessment and
Monitoring of Air Pollution Effects on Forests
Thünen – Institut für Weltforstwirtschaft
Leuschnerstr. 91
21031 Hamburg
GERMANY
Phone: +49 40 739 62 100/Fax: +49 40 739 62 199
e-mail: [email protected]
Mr Michael Köhl, Chairman of ICP Forests
ICP Forests
Lead Country
International Co-operative Programme on Assessment and
Monitoring of Air Pollution Effects on Forests
Bundesministerium für Ernährung,
Landwirtschaft und Verbraucherschutz – Ref. 535
Postfach 14 02 70
53107 BONN
GERMANY
Phone: +49 228 99 529-41 30/Fax: +49 228-99 529 42 62
Ms Sigrid Strich
PCC of ICP Forests
Programme Coordinating Centre of ICP Forests
Thünen – Institut für Weltforstwirtschaft
Leuschnerstr. 91
21031 Hamburg
GERMANY
Phone: +49 40 739 62 140/Fax: +49 40 739 62 199
Internet: http://www.icp-forests.org
Mr Martin Lorenz
139
140
Annex III-2: Expert Panels, WG and other Coordinating Institutions
Expert Panel
Research Institute for Nature and Forest
on Soil and Soil Solution Environment & Climate Unit
Gaverstraat 4
9500 GERAARDSBERGEN
BELGIUM
Phone: +32 54 43 71 20/Fax: +32 54 43 61 60
Mr Bruno De Vos, Chair
Finnish Forest Research Institute Metla
PL 18
01301 VANTAA
FINLAND
Phone: +358 10 211 5457/Fax: +358 10 211 2103
Ms Tiina Nieminen, Co-chair
Expert Panel
on Foliar Analysis
and Litterfall
Finnish Forest Research Institute
Northern Unit
Eteläranta 55
96300, ROVANIEMI
FINLAND
Phone: +358 50 391 4045/Fax: +358 10 211 4401
Mr Pasi Rautio, Chair
Bundesforschungs- und Ausbildungszentrum für
Wald, Naturgefahren und Landschaft (BFW)
Seckendorff-Gudent-Weg 8
1131 WIEN
AUSTRIA
Phone: +43 1 878 38 114/Fax: +43 1 878 38 1250
Mr Alfred Fürst, Co-chair Foliage
Finnish Forest Research Institute Metla
PL 18
01301 VANTAA
FINLAND
Phone: +358 10 211 5115/Fax: +358 10 211 2103
Ms Liisa Ukonmaanaho, Co-chair Litterfall
Expert Panel
on Forest Growth
Eidgenössische Forschungsanstalt für Wald,
Schnee und Landschaft WSL
Zürcherstr. 111
8903 BIRMENSDORF
SWITZERLAND
Phone: +41 44 73 92 594/Fax: +41 44 73 92 215
Mr Matthias Dobbertin, Chair
1131 WIEN
AUSTRIA
Phone: +43 1 878 38 13 27/Fax: +43 1 878 38 12 50
Mr Markus Neumann, Co-chair
Expert Panel
on Deposition
Measurements
IVL Swedish Environmental Research Institute
Natural Resources & Environmental Research Effects
Box 210 60
100 31 STOCKHOLM
SWEDEN
Phone: +46 859 85 64 25(direct) and +46 859 85 63 00
Fax: +46 859 85 63 90
Email: [email protected]
Ms Karin Hansen, Chair Expert Panel Depostion
Slovenian Forestry Institute
Gozdarski Inštitut Slovenije GIS
Večna pot 2
1000 LJUBLJANA
SLOVENIA
Phone: +38 6 12 00 78 00/Fax: +38 6 12 57 35 89
Mr Daniel Zlindra, Co-chair
Expert Panel on
Ambient Air Quality
Schnee und Landschaft (WSL)
Zürcherstr. 111
8903 BIRMENSDORF
SWITZERLAND
Phone: +41 44 73 92 564/Fax: +41 44 73 92 215
Mr Marcus Schaub, Chair
141
142
Fundación Centro de Estudios Ambientales
del Mediterráneo - CEAM
Parque Tecnológico
C/ Charles R. Darwin, 14
46980 PATERNA - VALENCIA
SPAIN
Phone: +34 961 31 82 27/Fax: +34 961 31 81 90
Mr Vicent Calatayud, Co-chair
Expert Panel
on Crown Condition
Assessment and Damage
Types
Gaverstraat 4
9500 GERAARDSBERGEN
BELGIUM
Tel. +32 54 43 71 15/Fax: +32 54 43 61 60
Mr Peter Roskams, Chair
Servicio de Sanidad Forestal y Equilibrios Biológicos (SSF),
Dirección General de Medio Natural y Política Forestal
(Ministerio de Medio Ambiente y Medio Rural y Marino)
Forest Health Unit (DG Nature and Forest Policy)
Rios Rosas, 24, 6a pl.
28003 MADRID
SPAIN
Phone: +34 91 749 38 12/Fax: +34 91 749 38 77
e-mail: [email protected],
Mr Gerardo Sánchez, Co-chair
Expert Panel on Biodiversity and Ground
Vegetation Assessment
Coillte Teoranta
Research and Development
Dublin Road
Newtown Mt. Kennedy
CO. WICKLOW
IRELAND
Phone: +353 120 11 162/Fax: +353 120 11 199
Mr Pat Neville, Chair
Camerino University
Dept. of Environmental Sciences
Via Pontoni, 5
I - 62032 Camerino (MC)
ITALY
Phone: +39 0737404503/5/Fax: +39 0737404508
Mr Roberto Canullo, Chair
Committee on
Quality Assurance
143
TerraData Environmetrics srl
Via L. Bardelloni 19
58025 Monterotondo Marittimo (GR)
ITALY
Phone: +39 056 691 66 81
Mr Marco Ferretti, Chair
Nordwestdeutsche Forstliche Versuchsanstalt
Grätzelstraße 2
37079 Göttingen
GERMANY
Phone: +49 551 69 40 11 41/Fax. +49 551 69 40 11 60
Mr Nils König, Co-chair
WG on Quality Assurance
and Quality Control in
Laboratories
Grätzelstraße 2
37079 Göttingen
GERMANY
Phone: +49 551 69 40 11 41/Fax. +49 551 69 40 11 60
Mr Nils König, Chair
Forest Research Institute
Sękocin Stary, 3 Braci Leśnej Street
05-090 RASZYN
POLAND
Phone: +48 22 71 50 300/Fax: +48 22 72 00 397
Ms Anna Kowalska, Co-chair
Expert Panel on
Meteorology and
Phenology
Bayerische Landesanstalt für Wald und Forstwirtschaft (LWF)
Hans-Carl-von-Carlowitz-Platz 1
85354 Freising
GERMANY
Phone: +49 (81 61) 71 49 21 / Fax: +49 (81 61) 71 49 71
Mr Stephan Raspe, Chair
Večna pot 2
SI-1000 LJUBLJANA
SLOVENIA
Phone: +386 (1) 200 78 46 149 / Fax: +386 (1) 257 35 89
Ms Urša Vilhar, Co-chair Phenology
144
Forest Foliar Coordinating Center (FFCC)
1131 WIEN
AUSTRIA
Phone: +43 1 878 38 11 14/Fax: +43 1 878 38 12 50
Mr Alfred Fürst
Forest Soil Coordinating
Centre
Research Institute for Nature and Forest (INBO)
Gaverstraat 4
9500 GERAARDSBERGEN
BELGIUM
Phone: +32 54 43 61 75/Fax: +32 54 43 61 89
Ms Nathalie Cools
Annex III-3: Ministries (Min) and National Focal Centres (NFC)
Albania
(Min)
Ministry of the Environment, Forests and Water Administration
Dep. of Biodiversity and Natural Resources Management
Durresi Str., Nr. 27
TIRANA
ALBANIA
Phone: +355 42 70 621, +355 42 70 6390
Fax: +355 42 70 627
(NFC)
Forest and Pasture Research Institute
"Halil Bego" Str, L. 23
TIRANA
ALBANIA
Phone: +355 437 12 42/Fax +355 437 12 37
[email protected]
Andorra
(Min)
(NFC)
Ministeri de Medi Ambient, Agricultura i Patrimoni
Natural Govern d'Andorra, Departament de Medi Ambient
Tècnica de l'Àrea d'Impacte Ambiental
C. Prat de la Creu, 62-64
500 ANDORRA LA VELLA
PRINCIPAT D'ANDORRA
Phone: +376 87 57 07/Fax: +376 86 98 33
e-mail: [email protected], [email protected]
Ms Silvia Ferrer, Ms Anna Moles
Austria
(NFC)
Bundesforschungs- und Ausbildungszentrum für Wald,
Naturgefahren und Landschaft (BFW)
1131 WIEN
AUSTRIA
Phone: +43 1 878 38 13 30/Fax: +43 1 878 38 12 50
Mr Ferdinand Kristöfel
(Min)
Bundesministerium für Land- und Forstwirtschaft,
Umwelt und Wasserwirtschaft, Abt. IV/2
Stubenring 1
1010 WIEN
AUSTRIA
Phone: +43 1 71 100 72 14/Fax: +43 1 71 10 0 0
Mr Vladimir Camba
Belarus
(NFC)
Forest inventory republican unitary company
"Belgosles"
Zheleznodorozhnaja st. 27
220089 MINSK
BELARUS
Phone: +375 17 22 63 053/Fax: +375 17 226 30 92
Mr Valentin Krasouski
(Min)
Committee of Forestry
Myasnikova st. 39
220048 MINSK
BELARUS
Phone/Fax: +375 172 00 45 82
Mr Petr Semashko
Belgium
Wallonia
(Min)
(NFC)
Service public de Wallonie (SPW)
Direction générale opérationnelle Agriculture,
Ressources naturelles et Environnement (DGARNE)
Département de la Nature et des Forêts - Direction des Ressources Forestières
Avenue Prince de Liège 15
5100 JAMBES
BELGIUM
Phone: +32 (81) 33 58 42 and +32 (81) 33 58 34
Fax: +32 (81) 33 58 11
Mr Christian Laurent, Mr Etienne Gérard, Mr. Mathieu Jonard
145
146
(NFC)
Earth and Life Institute, Environmental Sciences
Université catholique de Louvain
Croix du Sud, 2 - L7.05.09
1348 LOUVAIN-LA-NEUVE
BEGLIUM
Phone: +32 10 47 37 02 and +32 10 47 25 48
Fax: +32 10 47 36 97
Ms Isabelle Caignet, Mr Mathieu Jonard
Flanders
(Min)
Ministry of the Flemish Region (AMINAL)
Flemish Forest Service
Koning Albert II-laan 20 bus 22
1000 BRUSSELS
BELGIUM
Phone: +32 2 553 81 02/Fax: +32 2 553 81 05
Mr Carl De Schepper
Flanders
(NFC)
Gaverstraat 4
9500 GERAARDSBERGEN
BELGIUM
Phone: +32 54 43 71 15/Fax: +32 54 43 61 60
Mr Peter Roskams
Bulgaria
(NFC)
Executive Environment Agency
Monitoring of Lands, Biodiversity and Protected Areas Department
136 "Tzar Boris III" Blvd., P.O. Box 251
1618 SOFIA
BULGARIA
Phone: +359 2 940 64 86/Fax:+359 2 955 90 15
Ms. Genoveva Popova
(Min)
Ministry of Environment and Water
National Nature Protection Service
22, Maria Luiza Blvd.
1000 SOFIA
BULGARIA
Phone: + 359 2 940 61 12/Fax: +359 2 940 61 27
Ms. Penka Stoichkova
Canada
(Min)
(NFC)
Natural Resources Canada
580 Booth Str., 12th Floor
OTTAWA, ONTARIO K1A 0E4
CANADA
Phone: +1 (613) 947 90 60/Fax: +1 (613) 947 90 35
Mr Pal Bhogal
Québec
(Min)
(NFC)
Ministère des Ressources naturelles
Direction de la recherche forestière
2700, rue Einstein, bureau RC. 102
STE. FOY (QUEBEC) G1P 3W8
CANADA
Phone: +1 418 643 79 94 Ext. 65 33/Fax: +1 418 643 21 65
Mr Rock Ouimet
Croatia
(Min)
(NFC)
Hrvatski šumarski institut
Croatian Forest Research Institute
Cvjetno naselje 41
10450 JASTREBARSKO
CROATIA
Phone: +385 1 62 73 027/Fax: + 385 1 62 73 035
Mr Nenad Potocic
Cyprus
(Min)
(NFC)
Ministry of Agriculture,
Natural Resources and Environment
Research Section - Department of Forests
Louki Akrita 26
1414-NICOSIA
CYPRUS
Phone: +357 22 81 94 90/Fax: +357 22 30 39 35
Mr Andreas Christou
Czech Republic
(NFC)
Forestry and Game Management
Research Institute (VULHM)
Jíloviště-Strnady 136
PRAGUE 5 – Zbraslav
PSČ 156 04
CZECH REPUBLIC
Phone: +420 257 89 22 21/Fax: +420 257 92 14 44
Mr Bohumír Lomský
(Min)
Ministry of Agriculture of the Czech Republic
Forest Management
Tešnov 17
117 05 PRAGUE 1
147
148
CZECH REPUBLIC
Phone: +420 221 81 11 11/Fax: +420 221 81 29 88
Mr Tomáš Krejzar
Denmark
(NFC)
Forest & Landscape Frederiksberg
University of Copenhagen
Rolighedsvej 23
1958 Frederiksberg C
DENMARK
Phone: +45 35 33 18 97/Fax: +45 35 33 15 08
Mr Morten Ingerslev
(Min)
Danish Ministry of the Environment, Nature Agency
Haraldsgade 53
2100 Copenhagen
DENMARK
Phone: +45 72 54 30 00
Ms Agnete Thomsen
Estonia
(NFC)
Estonian Environment Information Centre
Rõõmu tee 2
51013 TARTU
ESTONIA
Phone:+37 27 33 97 13/Fax: +37 27 33 94 64
Mr Kalle Karoles
(Min)
Ministry of the Environment
Forest and Nature Conservation Department
Narva mnt 7a
15172 TALLINN
ESTONIA
Phone: +27 2 626 29 13/Fax: +27 2 626 28 01
Mr Andres Talijärv
Finland
(NFC)
Finnish Forest Research Institute
(METLA)
Parkano Research Unit
Kaironiementie 15
39700 PARKANO
FINLAND
Phone: +358 10 211 40 61/Fax: +358 10 211 40 01
Ms Päivi Merilä
(Min)
Ministry of Agriculture and Forestry
Forest Department
Hallituskatu 3 A
00023 GOVERNMENT
FINLAND
Phone: +358 9 160 523 19/Fax +358 9 160 52 400
Mr Teemu Seppä
France
(NFC)
Office National des Forêts
Direction technique et commerciale bois
Département recherche - Bâtiment B
Boulevard de Constance
77300 Fontainebleau
FRANCE
Phone: +33 1 60 74 92-28/Fax: +33 1 64 22 49 73
Mr Manuel Nicolas
(Min)
Ministère de l'alimentation, de l'agriculture et de la pêche
Direction générale de l'alimentation
Sous-Direction de la qualité et de la protection des végétaux
Département de la santé des forêts
251 rue de Vaugirard
75732 Paris cedex 15
FRANCE
Phone: +33 1 49 55 51 95/Fax: +33 1 49 55 59 49
[email protected]
Mr Jean-Luc Flot, Mr Fabien Caroulle
Germany
(Min)
(NFC)
Bundesministerium für Ernährung,
Landwirtschaft und Verbraucherschutz – Ref. 535
Rochusstr. 1
53123 BONN
GERMANY
Phone: +49 228 99 529-41 30/Fax: +49 228 99 529-42 62
Ms Sigrid Strich
Greece
(NFC)
Forest Research Institute of Athens
National Agricultural Research Foundation
Terma Alkmanos str.
11528 ILISSIA, ATHENS
GREECE
Phone: +30 210 77 84 850, +30 210 77 84 240
Fax: +30 210 77 84 602
Mr George Baloutsos, Mr. Anastasios Economou,
Mr Panagiotis Michopoulos
149
150
(Min)
Ministry of Rural Development and Foods
Gen. Secretariat for Forests and the Natural Environment
Dir. of Forest Resources Development
Halkokondili 31
101 64 ATHENS
GREECE
Phone: +30 210 52 42 349/Fax: +30 210 52 44 135
Mr Panagiotis Balatsos, Mrs Sofia Kollarou
Hungary
(NFC)
State Forest Service
Central Agricultural Office (CAO), Forestry Directorates
Széchenyi u. 14
1054 BUDAPEST
HUNGARY
Phone: +36 1 37 43 216/Fax: +36 1 37 43 206
Mr László Kolozs
(Min)
Ministry of Agriculture and Rural Development
Department of Natural Resources
Kossuth Lajos tér 11
1055 BUDAPEST
HUNGARY
Phone: +36 1 301 40 25/Fax: +36 1 301 46 78
Mr András Szepesi
Ireland
(NFC)
Coillte Teoranta
Research & Environment
Dublin Road
Newtown Mt. Kennedy
CO. WICKLOW
IRELAND
Phone: + 353 1 20 111 62/Fax: +353 1 20 111 99
Mr Pat Neville
(Min)
Forest Service
Department of Agriculture, Fisheries and Food
Mayo West
Michael Davitt House
CASTLEBAR, CO. MAYO
IRELAND
Phone: +353 94 904 29 25/Fax: +353 94 902 36 33
Ms Orla Fahy
Italy
(Min)
(NFC)
Ministry for Agriculture and Forestry Policies
Corpo Forestale dello Stato
National Forest Service, Headquarter, Division 6^ (NFI,
CONECOFOR Service and forest monitoring)
via G. Carducci 5
00187 ROMA
ITALY
Phone: +39 06 466 570 43/Fax: +39 06 481 89 72
Mr Enrico Pompei
Latvia
(Min)
Latvian State Forest Research Institute “Silava”
Rigas Street 111
LV-2169, SALASPILS
LATVIA
Phone: +371 679 42 555/Fax: +371 679 01 359
Ms Lasma Abolina
(NFC)
Latvian State Forest Research „Silava
Rigas Street 111, Salaspils
LV-2169, Latvia
Phone: +37 1 67 94 25 55/Fax: +37 1 67 90 13 59
Mrs Zane Libiete-Zalite
Liechtenstein
(Min)
(NFC)
Amt für Wald, Natur und Landschaft
Dr. Grass-Strasse 10
9490 VADUZ
FÜRSTENTUM LIECHTENSTEIN
Phone: +423 236 64 01/Fax: +423 236 64 11
Mr Felix Näscher
Lithuania
(NFC)
State Forest Survey Service
Pramones ave. 11a
51327 KAUNAS
LITHUANIA
Phone: +370 37 49 02 90/Fax: +370 37 49 02 51
Mr Albertas Kasperavicius
(Min)
Ministry of Environment
Dep. of Forests and Protected Areas
A. Juozapaviciaus g. 9
2600 VILNIUS
LITHUANIA
Phone: +370 2 72 36 48/Fax: +370 2 72 20 29
Mr Valdas Vaiciunas
151
152
Luxembourg
(Min)
(NFC)
Administration de la nature et des forêts
Service des forêts
16, rue Eugène Ruppert
2453 LUXEMBOURG
LUXEMBOURG
Phone: +352 402 20 12 11/Fax: +352 402 20 12 50
Mr Marc Wagner
Former Yugoslav Republic of Macedonia
(FYROM)
(NFC)
Ss. Cyril and Methodius University in Skopje
Faculty of Forestry in Skopje
Department of Forest and Wood Protection
bul. Aleksandar Makedonski bb
1000 SKOPJE
FORMER YUGOSLAV REP. OF MACEDONIA
Phone: +389 2 313 50 03 150/Fax: +389 2 316 45 60
Mr Nikola Nikolov
(Min)
Ministry of Agriculture, Forestry and Water Economy
Dep. for Forestry and Hunting
2 Leninova Str.
1000 SKOPJE
FORMER YUGOSLAV REP. OF MACEDONIA
Phone/Fax: +398 2 312 42 98
Mr Vojo Gogovski
Republic of Moldova
(Min)
(NFC)
State Forest Agency
124 bd. Stefan Cel Mare
2001 CHISINAU
REPUBLIC OF MOLDOVA
Phone: +373 22 27 23 06/Fax: +373 22 27 73 45
Mr Anatolie Popusoi
The Netherlands
(NFC)
National Institute for Public Health and the Environment
(RIVM)
Antonie van Leeuwenhoeklaan 9
3721 MA Bilthoven
THE NETHERLANDS
Klaas van der Hoek
Norway
(NFC)
Norwegian Forest and Landscape Institute
Høgskoleveien 8
1432 ÅS
NORWAY
Phone: +47 64 94 89 92/Fax: +47 64 94 80 01
Mr Dan Aamlid
(Min)
Norwegian Pollution Control Authority (SFT)
Dep. for Environmental Strategy
Section for Environmental Monitoring
P.O. Box 8100 Dep
Strømsveien 96
0032 OSLO
NORWAY
Phone: +47 22 57 34 87/Fax: +47 22 67 67 06
Mr Tor Johannessen
Poland
(NFC)
Forest Research Institute
Instytut Badawczy Lesnictwa
Sękocin Stary
ul. Braci Leśnej nr 3
05-090 RASZYN
POLAND
Phone: +48 22 715 06 57/Fax: +48 22 720 03 97
Mr Jerzy Wawrzoniak
(Min)
Ministry of the Environment
Department of Forestry
Wawelska Str. 52/54
00 922 WARSAW
POLAND
Phone: +48 22 579 25 50/Fax: +48 22 579 22 90
Mr Edward Lenart
Portugal
(Min)
(NFC)
Autoridade Florestal Nacional / National Forest Authority
Ministério da Agricultura, do Desenvolvimento Rural e das
Pescas
Divisão de Protecção e Conservação Florestal
Av. João Crisóstomo, 26-28
1069-040 LISBOA
PORTUGAL
Phone: +351 21 312 49 58/Fax: +351 21 312 49 87
Ms Maria Barros
153
154
Romania
(Min)
(NFC)
Forest Research and Management Institute (ICAS)
Bd. Eroilor 128
077190 Voluntari, Judetul Ilfov
ROMANIA
Phone: +40 21 350 32 38/Fax: +40 21 350 32 45
Mr Ovidiu Badea, Mr Romica Tomescu
Russian Fed.
(Min)
Ministry of Natural Resources of the Russian Federation
4/6, B. Gruzinskaya Str.
MOSCOW D-242, GSP-5, 123995
RUSSIAN FEDERATION
Phone: +7 495 254 48 00/Fax: +7 495 254 43 10 and
+7 495 254 66 10
Mr Igor A. Korolev
(NFC)
Centre for Forest Ecology and Productivity
of the Russian Academy of Sciences
Profsouznaya str., 84/32
117 997 MOSCOW
RUSSIAN FEDERATION
Phone: +7 495 332 29 17/Fax: +7 495 332 26 17
Ms Natalia Lukina
Serbia
(Min)
Ministry of Agriculture, Forestry and Water Management
Directorate of Forests
Omladinskih brigada 1
11070 BELGRADE
REPUBLIC OF SERBIA
Phone: +381 11 311 75 66/Fax: +381 11 313 15 69
Mr Sasa Orlovic
(NFC)
Institute of Forestry
str. Kneza Viseslava 3
11000 BELGRADE
SERBIA
Phone: +381 11 3 55 34 54/Fax: + 381 11 2 54 59 69
Mr Radovan Nevenic
Slovakia
(NFC)
National Forest Centre - Forest Research Institute
Národné lesnícke centrum
ul. T.G. Masaryka 22
962 92 ZVOLEN
SLOVAKIA
Phone: +421 45 531 42 02 Fax: +421 45 531 41 92
Mr Pavel Pavlenda
(Min)
Ministry of Agriculture of the Slovak Republic
Dobrovičova 12
812 66 BRATISLAVA
SLOVAK REPUBLIC
Phone: +421 2 59 26 63 08/Fax: +421 2 59 26 63 11
Mr Juraj Balkovic
Slovenia
(NFC)
Gozdarski Inštitut Slovenije
Večna pot 2
1000 LJUBLJANA
SLOVENIA
Phone: +386 1 200 78 00/Fax: +386 1 257 35 89
Mr Marko Kovač
(Min)
Ministry of Agriculture, Forestry and Food (MKGP)
Dunajska 56-58
1000 LJUBLJANA
SLOVENIA
Phone: +386 1 478 90 38/Fax: +386 1 478 90 89
Mr Janez Zafran, Mr Robert Režonja
Spain
(NFC)
Servicio de Sanidad Forestal y Equilibrios Biológicos
(SSF), Dirección General de Dearollo Rural y Política Forestal, (Ministerio de Agricultura, Alimentación y Medio Ambiente)
Rios Rosas, 24, 6a pl.
28003 MADRID
SPAIN
Phone: +34 91 749 38 12; +34 91 49 37 20
Fax: +34 91 749 38 77
Mr Gerardo Sánchez, Ms Paloma García Fernández
155
156
(Min)
Dirección General de Dearrollo Rural y Política Forestal
Ministerio de Agricultura, Alimentacin y Medio Ambiente
C/Alfonso XII, 62 – 5a planta
28071 MADRID
SPAIN
Phone: +34 91 347 15 03
Fax: +34 91 564 52 35
Da Begoña Nieto Gilarte
Sweden
(Min)
(NFC)
Swedish Forest Agency
Vallgatan 6
551 83 JÖNKÖPING
SWEDEN
Phone: +46 36 35 93 85/Fax: +46 36 16 61 70
Mr Sture Wijk
Switzerland
(NFC)
Schnee und Landschaft (WSL)
Zürcherstr. 111
8903 BIRMENSDORF
SWITZERLAND
Phone: +41 44 739 25 02/Fax: +41 44 739 22 15
Mr Peter Waldner
(Min)
Eidgenössisches Departement für Umwelt, Verkehr, Energie
und Kommunikation (UVEK)
Bundesamt für Umwelt (BAFU)
Abteilung Wald
3003 BERN
SWITZERLAND
Phone: +41 31 322 05 18/Fax: +41 31 322 99 81
Ms Sabine Augustin
Turkey
(NFC)
General Directorate of Forestry
(NFC) Orman Genel Müdürlüğü
Orman İdaresi ve Planlama Dairesi Başkanlığı
Söğütözü Cad. No: 14/E Kat: 17
A ANKARA
TURKEY
Phone: +90 312 296 41 94/95, Fax: +90 312 296 41 96
Sitki Öztürk
(Min)
Ministry of Environment and Forestry
Çevre ve Orman Bakanlığı
Dumlupınar Bulvarı (Eskişehir Yolu 9. Km.) No:252
TOBB İkiz Kuleleri D Kule Kat: 21
BALGAT /ANKARA
TURKEY
Phone: +90 312 248 17 89 Fax: +90 312 248 18 02
Email: [email protected]
Mr Ahmet Karakas
Ukraine
(NFC)
Ukrainian Research Institute
of Forestry and Forest Melioration (URIFFM)
Laboratory of Forest Monitoring and Certification
Pushkinska Str. 86
61024 KHARKIV
UKRAINE
Phone: +380 57 707 80 57/Fax: +380 57 707 80
Mr Igor F. Buksha
(Min)
State Committee of Forestry of the Ukrainian Republic
9a Shota Rustaveli
01601, KIEV
UKRAINE
Phone: +380 44 235 55 63/Fax: +380 44 234 26 35
Mr Viktor P. Kornienko
United Kingdom
(NFC)
Forest Research Station Alice Holt Lodge
Gravehill Road, Wrecclesham
FARNHAM SURREY GU10 4LH
UNITED KINGDOM
Phone: +44 1 420 52 62 02/Fax: +44 1 420 23 653
Mr Andrew J Moffat
(Min)
Corporate and Forestry Support
Forestry Commission
231 Corstorphine Road
EDINBURGH EH12 7AT
UNITED KINGDOM
Phone: +44 0 131 314 63 54/Fax: +44 0 131 314 43 44
[email protected]
Mr Richard Howe
United States
of America
(NFC)
USDA Forest Service
Pacific Southwest Research Station
4955 Canyon Crest Drive
RIVERSIDE, CA 92507
UNITED STATES OF AMERICA
157
158
Phone: +1 951 680 15 62/Fax: +1 951 680 15 01
Mr Richard V. Pouyat
(Min)
US Forest Service
Environmental Science Research Staff
Rosslyn Plaza, Building C
1601 North Kent Street, 4th Fl.
ARLINGTON, VA 22209
UNITED STATES OF AMERICA
Phone: +1 703 605 52 86/Fax: +1 703 605 02 79
Mr Richard V. Pouyat
159
Annex III-4: Authors
Apuhtin, Vladislav
Estonian Environment Information Centre
Mustamäe tee 33
EE-10616 Tallinn
Estonia
Becher, Georg
Thünen-Institut für Weltforstwirtschaft
Leuschnerstrasse 91
D-21031 Hamburg
Germany
Benham, Sue
Forest Research
Alice Holt Lodge
Wrecclesham
Farnham Surrey
GU10 4lH
United Kingdom
Bleeker, Albert
ECN Energy Research Center of the Netherlands
Postbus 1
NL-1755 Zg Petten
The Netherlands
Calderisi, Marco
TerraData environmetrics
IT-58025 Monterotondo Marittimo (GR)
Italy
Clarke, Nicholas
Norwegian Forest and Landscape Institute,
P.O. Box 115
N-1511
Ås
Norway
Cools, Nathalie
INBO
Gaverstraat 4
B-9500 Geraardsbergen
Belgium
De Vos, Bruno
INBO
Gaverstraat 4
Belgium
160
Derome, Kirsti
Finnish Forest Research Institute, Metla
Eteläranta 55
FI-96301 Rovaniemi
Finland
Dietrich, Hans-Peter
Bayerische Landesanstalt für Wald und Forstwirtschaft
D-85354 Freising
Germany
Dobbertin, Matthias (†2012)
Swiss Federal Institute for Forest, Snow and Landscape
Research WSL
Zürcherstrasse 111
CH-8903 Birmensdorf
Switzerland
Ferretti, Marco
TerraData environmetrics
IT-58025 Monterotondo Marittimo (GR)
Italy
Fischer, Richard
Leuschnerstrasse 91
D-21031 Hamburg
Germany
Graf Pannatier, Elisabeth
Research WSL
Zürcherstrasse 111
CH-8903 Birmensdorf
Switzerland
Granke, Oliver
Kantstr. 11
D-24116 Kiel
Germany
Hansen, Karin
IVL Swedish Environmental Research Institute
Box 210 60
SE-100 31 Stockholm
Sweden
Iacoban, Carmen
Forest Research Station, National Forest and Management
Institut
Calea Bucovinei, 73 bis
RO-725100 Campulung Moldovenesc
Romania
Ingerslev, Morten
Forest & Landscape Denmark
Rolighedsvej 23
DK-1958 Frederiksberg C
Denmark
161
Ingerslev, Morten
Rolighedsvej 23
Denmark
Iost, Susanne
Universität Hamburg
Zentrum Holzwirtschaft
Leuschnerstr. 91
D-21031 Hamburg
Germany
Kindermann, Georg
Federal Research and Training Centre for Forests, Natural
Hazards and Landscape
A-1131 Wien
Austria
Kowalska, Anna
Forest Research Institut, Instytut Badawczy Lesnictwa
Sekocin Stary ul. Braci Lesnej 3
PL-05-090 Raszyn
Poland
Lachmanová, Zora
Forestry and Game Management Research Institute
156 04 Prague 5
Zbraslav
Czech Republic
Lazdiņš, Andis
LSFRI "Silava"
Riga street 111
Salaspils
LV-2169
Latvia
Lindroos, Antti-Jussi
The Finnish Forest Research Institute
PO Box 18 (Jokiniemenkuja 1)
FI-01301 Vantaa
Finland
Lorenz, Martin
Leuschnerstr. 91
D-21031 Hamburg
Germany
Marchetto, Aldo
National Research Council
Largo Tonolli 50
IT-28922 Verbania Pallanza
Italy
Meesenburg, Henning
Grätzelstr. 2
D-37079 Göttingen
Germany
162
Meesenburg, Henning
Grätzelstr. 2
D-37079 Göttingen
Germany
Meining, Stefan
Büro für Umweltüberwachung
Im Sauergarten 84
D-79112 Freiburg
Germany
Merilä, Päivi
P.O.Box 413
FI-90014 University of Oulu
Finland
Molina, Juan
TECMENA S.L. C/
Clara del Rey 22, 1º B.
ES-28002 Madrid
Spain
Nagel, Hans- Dieter
OEKO-DATA
Hegermühlenstr. 58
D-15344 Strausberg
Germany
Neumann, Markus
Bundesforschungs- und Ausbildungszentrum für Wald,
Naturgefahren und Landschaft Seckendorff-Gudent-Weg 8
A-1131 Vienna
Austria
Nevalainen, Seppo
P.O. Box 68
FI-80101 Joensuu
Finland
Nieminen, Tiina
Jokiniemenkuja 1
FI-01370 Vantaa
Finland
Pihl-Karlsson, Gunilla
IVL Swedish Environmental Research Institute Ltd.
P.O. Box 210 60
SE-100 31 Stockholm
Sweden
Raspe, Stephan
Bavarian State Institute of Forestry
D-85354 Freising
Germany
163
Rautio, Pasi
Eteläranta 55
FI-96301 Rovaniemi
Finland
Rogora, Michela
CNR Institute of Ecosystem Study
Largo Tonolli 50
IT-28922 Verbania Pallanza,
Italy
Roskams, Peter
INBO Instituut voor Natuur- en Bosonderzoek
Gaverstraat 35
Belgium
Scheuschner, Thomas
OEKO-DATA
Hegermühlenstr. 58
D-15344 Strausberg
Germany
Schimming, Claus-Georg
Universität Kiel, Ökologie-Zentrum
Olshausenstr. 40
D-24098 Kiel
Germany
Schmitt Oehler, Maria
Research WSL
Zürcherstrasse 111
CH-8903 Birmensdorf
Switzerland
Seidling, Walter
Thünen-Institut für Waldökologie und Waldinventuren
Alfred-Möller-Str. 1
D-16225 Eberswalde
Germany
Simoncic, Primoz
Vecna pot 2
SI-1000 Ljubljana
Slovenia
Thimonier, Anne
Research WSL
Zürcherstrasse 111
CH-8903 Birmensdorf
Switzerland
Vanguelova, Elena
Forest Research
Alice Holt Lodge
Farnham
Surrey GU10 4LH
United Kingdom
164
Verstraeten, Arne
INBO Instituut voor natuur- en besonderzoek
Gaverstraat 35
Belgium
Vesterdal, Lars
Rolighedsvej 23
Denmark
Waldner, Peter
Research WSL
Zürcherstrasse 111
CH-8903 Birmensdorf
Switzerland
Wilpert, Klaus von
Forstliche Versuchs- und Forschungsanstalt, Abt. Boden
und Umwelt
Wonnhaldestrasse 4
D-79100 Freiburg
Germany
Žlindra, Daniel
Slovenian Forestry Institute - SFI /Gozdarski Inštitut
Slovenije - GIS
Večna pot 2
SI-1000 Ljubljana
Slovenia
For further information please contact
Thünen-Institute for World Forestry
PCC of ICP Forests
Dr. M. Lorenz, Richard Fischer
Leuschnerstr. 91
21031 Hamburg, Germany
Internet:
http://www.icp-forests.net
165
Forest Condition in Europe
2012

Forest condition in Europe / 2012 Technical Report of ICP

Transcription

Similar documents

PFB_Master_FINGER WEG - MIL